title: DSResSol: A sequence-based solubility predictor created with Dilated Squeeze Excitation Residual Networks

abstract:
Protein solubility is an important thermodynamic parameter critical for the characterization of a proteins function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g. pharmaceutical) applications. Thus, a highly accurate in silico bioinformatics tool for predicting protein solubility from protein sequence is sought. In this study, we developed a deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks. The model captures the frequently occurring amino acid k-mers and their local and global interactions, and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve higher performance in comparison to existing deep learning-based models. DSResSol uses protein sequence as input, outperforming all available sequence-based solubility predictors by at least 5% in accuracy when the performance is evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins, but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for fast, reliable, and inexpensive prediction of a proteins solubility to guide experimental design.

AvailabilityThe source code, datasets, and web server for this model are available at https://github.com/mahan-fcb/DSResSol


title: An Association Test of the Spatial Distribution of Rare Missense Variants within Protein Structures Improves Statistical Power of Sequencing Studies

abstract: Over 90% of variants are rare, and 50% of them are singletons in the Alzheimers Disease Sequencing Project Whole Exome Sequencing (ADSP WES) data. However, either single variant tests or unit-based tests are limited in the statistical power to detect the association between rare variants and phenotypes. To best utilize rare variants and investigate their biological effect, we exam their association with phenotypes in the context of protein. We developed a protein structure-based approach, POKEMON (Protein Optimized Kernel Evaluation of Missense Nucleotides), which evaluates rare missense variants based on their spatial distribution on the protein rather than allele frequency. The hypothesis behind this is that the three-dimensional spatial distribution of variants within a protein structure provides functional context and improves the power of association tests. POKEMON identified four candidate genes from the ADSP WES data, namely two known Alzheimers disease (AD) genes (TREM2 and SORL) and two novel genes (DUSP18 and CSF1R). For known AD genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low frequency risk variants within these genes. DUSP18 has a cluster of variants primarily shared by case subjects around the ligand-binding domain, and this cluster is further validated in a replication dataset with a larger sample size. POKEMON is an open-source tool available at https://github.com/bushlab-genomics/POKEMON.

category: bioinformatics


title: Karyotype asymmetry in Cuscuta L. subgenus Pachystigma reflects its repeat DNA composition

abstract:
Cuscuta is a cytogenetically diverse genus, with karyotypes varying 18-fold in chromosome number and 89-fold in genome size. Each of its four subgenera also presents particular chromosomal features, such as bimodal karyotypes in Pachystigma. We used low coverage sequencing of the Cuscuta nitida genome (subgenus Pachystigma), as well as chromosome banding and molecular cytogenetics of three subgenus representatives, to understand the origin of bimodal karyotypes. All three species, C. nitida, C. africana (2n = 28) and C. angulata (2n = 30), showed heterochromatic bands mainly in the largest chromosome pairs. Eighteen satellite DNAs were identified in C. nitida genome, two showing similarity to mobile elements. The most abundant were present at the largest pairs, as well as the highly abundant ribosomal DNAs. The most abundant Ty1/Copia and Ty3/Gypsy elements were also highly enriched in the largest pairs, except for the Ty3/Gypsy CRM, which also labelled the pericentromeric regions of the smallest chromosomes. This accumulation of repetitive DNA in the larger pairs indicates that these sequences are largely responsible for the formation of bimodal karyotypes in the subgenus Pachystigma. The repetitive DNA fraction is directly linked to karyotype evolution in Cuscuta.

HighlightsCuscuta subgenus Pachystigma contains species with strikingly bimodal karyotypes. The emergence of these karyotypes is linked to the enrichment of varied repetitive sequences in the largest chromosomal pairs.

category: plant biology


title: Comparative analysis of machine learning and evolutionary optimization algorithms for precision tissue culture of Cannabis sativa: Prediction and validation of in vitro shoot growth and development based on the optimization of light and carbohydrate sources

abstract: Micropropagation techniques offer opportunity to proliferate, maintain, and study dynamic plant responses in highly controlled environments without confounding external influences, forming the basis for many biotechnological applications. With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve current methods while boosting our understanding of the underlying physiological processes. Unfortunately, due to the exorbitantly large array of factors influencing tissue culture, existing approaches to optimize in vitro methods are tedious and time-consuming. Therefore, there is great potential to use new computational methodologies for analysing data to develop improved protocols more efficiently. Here, we first tested the effects of light qualities using assorted combinations of Red, Blue, Far Red, and White spanning 0-100 mol/m2/s in combination with sucrose concentrations ranging from 1-6 % (w/v), totaling 66 treatments, on in vitro shoot growth, root development, number of nodes, shoot emergence, and canopy surface area. Collected data were then assessed using multilayer perceptron (MLP), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) to model and predict in vitro Cannabis growth and development. Based on the results, GRNN had better performance than MLP or ANFIS and was consequently selected to link different optimization algorithms (genetic algorithm, biogeography-based optimization, interior search algorithm, and symbiotic organisms search) for prediction of optimal light levels (quality/intensity) and sucrose concentration for various applications. Predictions of in vitro conditions to refine growth responses were subsequently tested in a validation experiment and data showed no significant differences between predicted optimized values and observed data. Thus, this study demonstrates the potential of machine learning and optimization algorithms to predict the most favourable light combinations and sucrose levels to elicit specific developmental responses. Based on these, recommendations of light and carbohydrate levels to promote specific developmental outcomes for in vitro Cannabis are suggested. Ultimately, this work showcases the importance of light quality and carbohydrate supply in directing plant development as well as the power of machine learning approaches to investigate complex interactions in plant tissue culture.


title: PRC1 Sustains the Memory of Neuronal Fate Independent of PRC2 Function

abstract: Polycomb repressive complexes (PRCs) 1 and 2 maintain stable cellular memories of early fate decisions by establishing heritable patterns of gene repression. PRCs repress transcription through histone modifications and chromatin compaction, but their roles in neuronal subtype diversification are poorly defined. We unexpectedly found that PRC2 is dispensable to preserve the morphogen-induced positional fates of spinal motor neurons (MNs), while PRC1 is essential for the specification of segmentally-restricted subtypes. Mutation of the core PRC1 component Ring1 in mice leads to increased chromatin accessibility and ectopic expression of a broad variety of fates determinants, including Hox transcription factors, while neuronal class-specific features are maintained. Loss of MN subtype identities in Ring1 mutants is due to the suppression of Hox networks by derepressed caudal Hox genes. These results indicate that PRC1 can function independently of de novo PRC2-dependent histone methylation to maintain chromatin topology and transcriptional memory at the time of neuronal differentiation.

category: neuroscience


title: Synergism vs Additivity - Defining the Interactions between Common Disinfectants

abstract:
Many of the most common disinfectant and sanitizer products are formulations of multiple antimicrobial compounds. Products claiming to contain synergistic formulations are common, although there is often little supporting evidence. The antimicrobial interactions of all pairwise combinations of common disinfectants (benzalkonium chloride, didecyldimethylammonium chloride, polyhexamethylene biguanide, chlorocresol and bronopol) were classified via checkerboard and validated by time-kill analyses. Combinations were tested against Acinetobacter baumannii NCTC 12156, Enterococcus faecalis NCTC 13379, Klebsiella pneumoniae NCTC 13443 and Staphylococcus aureus NCTC 13143. Synergistic interactions were only identified between cholorocresol with benzalkonium chloride, and chlorocresol with polyhexamethylene biguanide. Synergism was not ubiquitously demonstrated against all species tested and was on the borderline of the synergism threshold. These data demonstrate that synergism between disinfectants is uncommon and circumstantial. Most of the antimicrobial interactions tested were characterised as additive. We suggest that this is due to the broad, non-specific mechanisms associated with disinfectants not providing opportunity for the combined activities of these compounds to exceed the sum of their parts.

IMPORTANCEThe scarcity of observed synergistic interactions suggests that many disinfectant-based products may be misinterpreting combined mechanisms of interaction. We emphasise the need to correctly differentiate between additivity and synergism in antimicrobial formulations, as inappropriate classification may lead to unnecessary issues in the event of regulatory changes. Furthermore, we question the need to focus on synergism and disregard additivity when considering combinations of disinfectants, as the benefits that synergistic interactions provide are not necessarily relevant to the application of the final product.

category: microbiology


title: Multiple macroecological laws do characterize various aspects of microbiota dynamics

abstract: In this letter we address the potential confusion related to our recent demonstration that multiple macroecological laws describe short- and long-term dynamics of microbial communities. Specifically, we clarify that these laws, similarly to many other relationships observed in nature, are characterized not just by the existence of scaling, but also by certain characteristic values of the scaling exponents. By performing proper statistical analysis, we demonstrate that the relationships sensitive to temporal bacterial dynamics are not reproduced in the shuffled data. We also discuss that there is no clear evidence in the data that macroecological relationships in microbiota are primarily driven by external or environmental factors. Proper statistical analyses of the data suggest that the dynamics of gut microbiota, even on a constant diet, contains rich temporal structure. Therefore, it is likely that complex and non-linear internal dynamics may be primarily responsible for the observed macroecological laws in microbiota and other ecological communities.


title: Rapid and strain-specific resistance evolution of Staphylococcus aureus against inhibitory molecules secreted by Pseudomonas aeruginosa

abstract: Pseudomonas aeruginosa and Staphylococcus aureus frequently occur together in polymicrobial infections, and there is evidence that their interactions negatively affect disease outcome in patients. At the molecular level, interactions between the two bacterial taxa are well-described, with P. aeruginosa usually being the dominant species suppressing S. aureus through a variety of inhibitory molecules. However, in polymicrobial infections, the two species interact over prolonged periods of time, and S. aureus might evolve resistance against inhibitory molecules deployed by P. aeruginosa. Here, we used experimental evolution to test this hypothesis by exposing three different S. aureus strains (Cowan I, 6850, JE2) to the growth-inhibitory supernatant of P. aeruginosa PAO1 over 30 days. We found that all three S. aureus strains rapidly evolved resistance against inhibitory molecules and show that (i) adaptations were strain-specific; (ii) resistance evolution affected the expression of virulence traits; and (iii) mutations in membrane transporters were the most frequent evolutionary targets. Our work indicates that adaptations of S. aureus to co-infecting pathogens could increase virulence and decrease antibiotic susceptibility, because both virulence traits and membrane transporters involved in drug resistance were under selection. Thus, pathogen co-evolution could exacerbate infections and compromise treatment options.


title: A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

abstract: High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zurich.

category: genetics


title: Domain Organization of Lentiviral and Betaretroviral Surface Envelope Glycoproteins Modeled with AlphaFold

abstract:
The surface envelope glycoproteins of non-primate lentiviruses and betaretroviruses share sequence similarity with the inner proximal domain {beta}-sandwich of the human immunodeficiency virus type 1 (HIV-1) gp120 glycoprotein that faces the transmembrane glycoprotein as well as patterns of cysteine and glycosylation site distribution that points to a similar two-domain organization in at least some lentiviruses. Here, high reliability models of the surface glycoproteins obtained with the AlphaFold algorithm are presented for the gp135 glycoprotein of the small ruminant caprine arthritis-encephalitis (CAEV) and visna lentiviruses and the betaretroviruses jaagsiekte sheep retrovirus (JSRV), mouse mammary tumor virus (MMTV) and consensus human endogenous retrovirus type K (HERV-K). The models confirm and extend the inner domain structural conservation in these viruses and identify two outer domains with a putative receptor binding site in the CAEV and visna virus gp135. The location of that site is consistent with patterns of sequence conservation and glycosylation site distribution in gp135. In contrast, a single domain is modeled for the JSRV, MMTV and HERV-K betaretrovirus envelope proteins that is highly conserved structurally in the proximal region and structurally diverse in apical regions likely to interact with cell receptors. The models presented here identify sites in small ruminant lentivirus and betaretrovirus envelope glycoproteins likely to be critical for virus entry and virus neutralization by antibodies and will facilitate their functional and structural characterization.

ImportanceStructural information on the surface envelope proteins of lentiviruses and related betaretroviruses is critical to understand mechanisms of virus-host interactions. However, experimental determination of these structures has been challenging and only the structure of the human immunodeficiency virus type 1 gp120 has been determined. The advent of the AlphaFold artificial intelligence method for structure prediction allows high-quality modeling of the structures of small ruminant lentiviral and betaretroviral surface envelope proteins. The models are consistent with much of previously described experimental data, show regions likely to interact with receptors and identify domains that may be involved in mechanisms of antibody neutralization resistance in the small ruminant lentiviruses. The models will allow more precise design of mutants to further determine mechanisms of viral entry and immune evasion in this group of viruses and constructs for structure of these surface envelope proteins.


title: Neutrophil-epithelial interactions augment infectivity and pro-inflammatory responses to SARS-CoV-2 infection

abstract:
In response to viral infection, neutrophils release inflammatory mediators as part of the innate immune response, contributing to pathogen clearance through virus internalization and killing. Pre-existing co-morbidities, correlating to incidence of severe COVID-19, are associated with chronic airway neutrophilia and examination of COVID-19 lung tissue revealed a series of epithelial pathologies associated with infiltration and activation of neutrophils. To determine the impact of neutrophil-epithelial interactions on the infectivity and inflammatory response to SARS-CoV-2 infection, we developed a co-culture model of airway neutrophilia. We discovered that SARS-CoV-2 infection of the airway epithelium alone does not result in a notable release of pro-inflammatory cytokines, however in the presence of neutrophils, the inflammatory response is both polarized and significantly augmented, epithelial barrier integrity in impaired and viral load of the airway epithelium increased. This study reveals a key role for neutrophil-epithelial interactions in determining inflammation, infectivity, and outcomes in response to SARS-CoV-2 infection.

HighlightsO_LIWe have developed a model to study neutrophil-epithelial interactions which better reflects the in vivo situation than monocultures
C_LIO_LINeutrophils significantly augment SARS-CoV-2 mediated, pro-inflammatory cytokine release from the epithelium indicating a key interaction
C_LIO_LISARS-CoV-2 infection leads to a polarized inflammatory response in differentiated airway epithelium
C_LIO_LIDisruption of the epithelial barrier via addition of neutrophils or cytokines leads to increased infection
C_LIO_LIStudy reveals a key role for neutrophil-epithelial interactions in determining outcome/infectivity
C_LI

category: cell biology


title: Cryo-EM structure determination of small proteins by nanobody-binding scaffolds (Legobodies)

abstract: We describe a general method that allows structure determination of small proteins by single-particle cryo-electron microscopy (cryo-EM). The method is based on the availability of a target-binding nanobody, which is then rigidly attached to two scaffolds: (1) a Fab-fragment of an antibody directed against the nanobody, and (2) a nanobody-binding protein A fragment fused to maltose binding protein and Fab-binding domains. The overall ensemble of [~]120 kDa, called Legobody, does not perturb the nanobody-target interaction and facilitates particle alignment in cryo-EM image processing. The utility of the method is demonstrated for the KDEL receptor, a 23 kDa membrane protein, resulting in a map at 3.2[A] overall resolution with density sufficient for de novo model building, and for the 22 kDa RBD of SARS-CoV2 spike protein, resulting in a map at 3.6 [A] resolution that allows analysis of the binding interface to the nanobody. The Legobody approach thus overcomes the current size limitations of cryo-EM analysis.

category: biochemistry


title: Computational Prediction of Disease Detection and Insect Identification using Xception model

abstract: In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The deep learning architectures used in this study were Convolutional Neural Network, VGG16, InceptionV3, and Xception. VGG16, InceptionV3, and Xception are categorized as the pre-trained models based on CNN architecture. They follow the concept of transfer learning. Transfer learning is a technique which makes use of the learnings of the models previously trained on a base data and applies it to the present dataset. This is an efficient technique which gives us rapid results and improved performance. Two plant datasets have been used here for disease and insects. The results of the algorithms were then compared. Most successful one has been the Xception model which obtained 82.89 for disease and 77.9 for pests.


title: Visualizing conformational space of functional biomolecular complexes by deep manifold learning

abstract: The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important biomolecular complexes at atomic level is essential to understand their functional mechanisms and to guide structure-based drug discovery. Here we introduce a deep learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. By applications of this approach to analyze five experimental datasets, we demonstrate its generality in breaking resolution barrier of visualizing dynamic components of functional complexes, in choreographing continuous inter-subunit motions and in exploring their  hidden conformational space. Our approach expands the realm of structural ensemble determination to the nonequilibrium regime at atomic level, thus potentially transforming biomedical research and therapeutic development.


title: SteadyCellPhenotype: A web-based tool for the modeling of biological networks with ternary logic

abstract:
SummaryWe introduce SteadyCellPhenotype, a browser based interface for the analysis of ternary biological networks. It includes tools for deterministically finding all steady states of a network, as well as the simulation and visualization of trajectories with publication quality graphics. Stochastic simulations allow us to approximate the size of the basin for attractors and deterministic simulations of trajectories nearby specified points allow us to explore the behavior of the system in that neighborhood.

Availabilityhttps://github.com/knappa/steadycellphenotypeMITLicense

Contactchifman@american.edu


title: Neuronal mimicry generates an ecosystem critical for brain metastatic growth of SCLC

abstract: Brain metastasis is a major cause of morbidity and mortality in cancer patients. Here we investigated mechanisms allowing small-cell lung cancer (SCLC) cells to grow in the brain. We show that SCLC cells undergo a cell state transition towards neuronal differentiation during tumor progression and metastasis, and that this neuronal mimicry is critical for SCLC growth in the brain. Mechanistically, SCLC cells re-activate astrocytes, which in turn promote SCLC growth by secreting neuronal pro-survival factors such as SERPINE1. We further identify Reelin, a molecule important in brain development, as a factor secreted by SCLC cells to recruit astrocytes to brain metastases in mice. This recruitment of astrocytes by SCLC was recapitulated in assembloids between SCLC aggregates and human cortical spheroids. Thus, SCLC brain metastases grow by co-opting mechanisms involved in reciprocal neuron-astrocyte interactions during development. Targeting such developmental programs activated in this cancer ecosystem may help treat brain metastases.

category: cancer biology


title: Characterizing the performance of human leg external force control

abstract: Our legs act as our primary contact with the surrounding environment, generating external forces that enable agile motion. To be agile, the nervous system has to control both the magnitude of the force that the feet apply to the ground and the point of application of this force. The purpose of this study was to characterize the performance of the healthy human neuromechanical system in controlling the force-magnitude and position of an externally applied force. To accomplish this, we built an apparatus that immobilized participants but allowed them to exert variable but controlled external forces with a single leg onto a ground embedded force plate. We provided real-time visual feedback of either the leg force-magnitude or position that participants were exerting against the force platform and instructed participants to best match their real-time signal to prescribed target step functions. We tested target step functions of a range of sizes and quantified the responsiveness and accuracy of the control. For the control of force-magnitude and for intermediate step sizes of 0.45 bodyweights, we found a bandwidth of 1.8{+/-}0.5 Hz, a steady-state error of 2.6{+/-}0.9%, and a steady-state variability of 2.7{+/-}0.9%. We found similar control performance in terms of responsiveness and accuracy across step sizes and between force-magnitude and position control. Increases in responsiveness correlated with reductions in other measures of control performance, such as a greater magnitude of overshooting. We modelled the observed control performance and found that a second-order model was a good predictor of external leg force control. We discuss how benchmarking force control performance in young healthy humans aids in understanding differences in agility between humans, between humans and other animals, and between humans and engineered systems.

category: physiology


title: Diffusely abnormal white matter converts to T2 lesion volume in the absence of acute inflammation

abstract:
Diffusely abnormal white matter (DAWM), characterised by biochemical changes of myelin in the absence of frank demyelination, has been associated with clinical progression in secondary progressive MS (SPMS). However, little is known about changes of DAWM over time and their relation to focal white matter lesions (FWML).

The objectives of this work were: 1) To characterize the longitudinal evolution of FWML, DAWM, and DAWM that transforms into FWML, and 2) To determine whether gadolinium enhancement, known to be associated with the development of new FWML, is also related to DAWM voxels that transform into FWML.

Our data included 4220 MRI scans of 689 SPMS participants, followed for 156 weeks and 2677 scans of 686 RRMS participants, followed for 96 weeks. FWML and DAWM were segmented using a previously validated, automatic thresholding technique based on normalized T2 intensity values. Using longitudinally registered images, DAWM voxels at each visit that transformed into FWML on the last MRI scan as well as their overlap with gadolinium enhancing lesion masks were identified.

Our results showed that the average yearly rate of conversion of DAWM-to-FWML was 1.27cc for SPMS and 0.80cc for RRMS. FWML in SPMS participants significantly increased (t=3.9; p=0.0001) while DAWM significantly decreased (t=-4.3 p<0.0001) and the ratio FWML:DAWM increased (t=12.7; p<0.00001). RRMS participants also showed an increase in the FWML:DAWM Ratio (t=6.9; p<0.00001) but without a significant change of the individual volumes. Gadolinium enhancement was associated with 7.3% and 18.7% of focal New T2 lesion formation in the infrequent scans of the RRMS and SPMS cohorts, respectively. In comparison, only 0.1% and 0.0% of DAWM-to-FWML voxels overlapped with gadolinium enhancement.

We conclude that DAWM transforms into FWML over time, in both RRMS and SPMS. DAWM appears to represent a form of pre-lesional pathology that contributes to T2 lesion volume increase over time, independent of new focal inflammation and gadolinium enhancement.


title: Visual evoked feedforward-feedback travelling waves organize neural activity across the cortical hierarchy in mice

abstract: Sensory processing is distributed among many brain regions that interact via feedforward and feedback signaling. It has been hypothesized that neuronal oscillations mediating feedforward and feedback interactions organize into travelling waves. However, stimulus evoked travelling waves of sufficient spatial scale have never been demonstrated directly. Here, we show that simple visual stimuli reliably evoke two traveling waves with spatial wavelengths that cover much of the cerebral hemisphere in awake mice. 30-50Hz feedforward waves arise in primary visual cortex (V1) and propagate rostrally, while 3-6Hz feedback waves originate in the association cortex and flow caudally. The phase of the feedback wave modulates the amplitude of the feedforward wave and synchronizes firing between V1 and parietal cortex. Altogether, these results provide direct experimental evidence that visual evoked travelling waves percolate through the cerebral cortex and coordinate neuronal activity across broadly distributed networks mediating visual processing.


title: Antibodies control metabolism by regulating insulin homeostasis

abstract: Homeostasis of metabolism by hormone production is crucial to maintain physiological integrity and disbalance can cause severe metabolic disorders such as diabetes mellitus. Here, we show that antibodies recognizing insulin are key regulators of blood glucose and metabolism controlling insulin concentrations. In fact, antibody-deficient mice and immunodeficiency patients show sub-physiological blood glucose, which becomes normal after total IgG injection. We show that insulin-specific IgG antibodies found in the serum of wildtype mice or healthy individuals are responsible for this regulation. Interestingly, we identify two fractions of anti-insulin IgM which differ in their affinity to insulin. The low affinity IgM fraction (anti-insulin IgMlow) neutralizes insulin and leads to increased blood glucose while the high affinity IgM fraction (anti-insulin IgMhigh) protects insulin from neutralization by anti-insulin IgG thereby preventing blood glucose dysregulation. In contrast to anti-insulin IgMhigh, anti-insulin IgMlow binds to dsDNA suggesting that it is multi-specific. This multi-specificity mediates the formation of larger immune complexes containing insulin which results in increased uptake and degradation of insulin by macrophages in the presence of anti-insulin IgMlow as compared to anti-insulin IgMhigh. To demonstrate that high affinity anti-insulin IgM acts as protector of insulin and counteracts insulin neutralization by anti-insulin IgG, we expressed the variable regions of the same anti-insulin antibody as IgG or IgM. Strikingly, only the anti-insulin IgM regulated insulin function and prevented IgG-mediated neutralization of insulin and subsequent blood glucose dysregulation. Since anti-insulin IgMhigh is generated in the course of an immune response and affinity maturation, its protective role suggests that preventing autoimmune damage and maintaining physiological homeostasis requires adaptive tolerance mechanisms that generate protective IgM antibodies during memory responses.

category: immunology


title: In-field capable loop-mediated isothermal amplification detection of fall army worm (Spodoptera frugiperda; Lepidoptera: Noctuidae) larvae using a rapid and simple crude extraction technique

abstract: Fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), is an economically important pest worldwide and has recently been identified in Australia. Morphological identification of S. frugiperda at early larval stages can be difficult often requiring expert microscopy analysis. Rapid and accurate in-field diagnosis is vital for management decision support and there are no tools currently available for this purpose. In this study, a sensitive, specific and in-field capable loop-mediated isothermal amplification (LAMP) assay was developed to detect S. frugiperda larvae. A primer set based on a highly conserved region of the S. frugiperda cytochrome oxidase subunit 1 (COX1) gene provided detection within 30 min from both total DNA and crude extractions. The crude extraction technique of crushing 10 mg of S. frugiperda material in 50 L ddH20 and further diluting the homogenate in ddH20 is rapid, simple and does not require heat blocks, centrifuges or special buffers increasing its utility as a field-based technique. The primer set detected as little as 24 pg of S. frugiperda DNA and did not cross-react with any other of the lepidopteran species tested that are easily confused with S. frugiperda in Australia. Therefore, this assay could be used in-field to correctly identify the presence of S. frugiperda and thereby greatly assist with timely management decisions.

category: molecular biology


title: Matthew effect: common species become more common and rare ones become more rare in response to artificial light at night

abstract:
Artificial light at night (ALAN) has been and still is rapidly spreading, and has become an important component of global change. Although numerous studies have tested its potential biological and ecological impacts on animals, fewer have tested its impacts on plants, and very few studies have tested whether it affects alien and native plants differently. Furthermore, common plant species, and particularly common alien species, are often found to benefit more from additional resources than rare native and rare alien species. Whether this is also the case with regard to increasing light due to ALAN is still unknown.

Here, we tested how ALAN affects the performance of common and rare alien and native plants directly and indirectly via flying insects. We grew five common alien, six rare alien, five common native and four rare native plant species under four combinations of two ALAN (no ALAN vs ALAN) and two insect-exclusion (no exclusion vs exclusion) treatments, and compared their biomass production.

We found that common plant species, irrespective of whether they are alien or native, produced significantly more biomass than rare species, particularly under ALAN. Furthermore, alien species tended to show a slightly stronger positive response to ALAN than native species (marginally significant origin x ALAN interaction, p = 0.079).

Our study shows that common plant species benefited more from ALAN than rare ones. This might lead to shifts in plant diversity and vegetation composition, further propelling global biodiversity decline, when ALAN becomes more widespread. In addition, the slightly more positive response of alien species indicates that ALAN might increase the risk of alien plant invasions.

category: ecology


title: isotracer: An R package for the analysis of tracer addition experiments

abstract:
O_LITracer addition experiments, particularly using isotopic tracers, are becoming increasingly important in a variety of studies aiming at characterizing the flows of molecules or nutrients at different levels of biological organization, from the cellular and tissue levels, to the organismal and ecosystem levels.
C_LIO_LIWe present an approach based on Hidden Markov Models (HMM) to estimate nutrient flow parameters across a network, and its implementation in the R package isotracer.
C_LIO_LIThe isotracer package is capable of handling a variety of tracer study designs, including continuous tracer drips, pulse experiments, and pulse-chase experiments. It can also take into account tracer decay when radioactive isotopes are used.
C_LIO_LITo illustrate its use, we present three case studies based on published data and spanning different levels of biological organization: a molecular-level study of protein synthesis and degradation in Arabidopsis thaliana, an organismal-level study of phosphorus incorporation in the eelgrass Zostera marina, and an ecosystem-level study of nitrogen dynamics in Trinidadian montane streams.
C_LIO_LIWith these case studies, we illustrate how isotracer can be used to estimate uptake rates, turnover rates, and total flows, as well as their uncertainty. We also show how to perform model selection to compare alternative hypotheses.
C_LIO_LIWe conclude by discussing isotracers further applications, limitations, and possible future improvements and expansions.
C_LI


title: Fingolimod Regulates BDNF-AS and HOTAIR Expression in Multiple Sclerosis Patients

abstract: Brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family, is expressed by neurons and glial cells in the central nervous system (CNS). In the CNS, BDNF is responsible for neuroprotection and neurogenesis. Recent studies showed that the Fingolimod, the first oral medicine for relapsing-remitting multiple sclerosis (RR-MS), induces BDNF expression. Besides, It is well demonstrated that long noncoding RNAs (lncRNAs) have a pivotal role in gene regulation. This study is mainly focused on how Fingolimod treatment plays role in BDNF regulation in coordination with lncRNAs. An in-silico study was performed to predict BDNF-regulatory candidate lncRNAs using online tools. Then, the expression of BDNF-related lncRNAs was analyzed in patients with relapsing-remitting multiple sclerosis (RRMS) at baseline and after three months of Fingolimod treatment. Based on in silico results, two lncRNAs with potential regulatory functions on the BDNF including, Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) and HOX Transcript Antisense RNA (HOTAIR), and also natural antisense of BDNF were selected. Fingolimod treatment increased the expression of HOTAIR lncRNA; however, the BDNF antisense RNA (BDNF-AS) expression was reduced dramatically. Furthermore, the results indicate a positive correlation between HOTAIR and MALAT1 lncRNAs and BDNF. Also, after Fingolimod treatment, the patients EDSS scores were declined or remained unchanged, indicating disease hindrance by Fingolimod therapy. Altogether, fingolimod exerts protective roles in RRMS patients probably by the mediation of HOTIAR and BDNF-AS lncRNAs.


title: A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer

abstract: The development and clinical implementation of evidence-based precision medicine strategies has become a realistic possibility, primarily due to the rapid accumulation of large-scale genomics and pharmacological data from diverse model systems: patients, cell-lines and drug perturbation studies. We introduce a novel Bayesian modeling framework called the individualized theRapeutic index (iRx) model to integrate high-throughput pharmacogenomic data across model systems. Our iRx model achieves three main goals: first, it exploits the conserved biology between patients and cell-lines to calibrate therapeutic response of drugs in patients; second, it finds optimal cell line avatars as proxies for patient(s); and finally, it identifies key genomic drivers explaining cell line-patient similarities. This is achieved through a semi-supervised learning approach, that conflates (unsupervised) sparse latent factor models with (supervised) penalized regression techniques. We propose a unified and tractable Bayesian model for estimation, and inference is conducted via efficient posterior sampling schemes. We illustrate and validate our approach using two existing clinical trial datasets in multiple myeloma and breast cancer studies. We show that our iRx model improves prediction accuracy compared to naive alternative approaches, and it consistently outperforms existing methods in literature in both in multiple simulation scenarios as well as real clinical examples.

category: genomics


title: Predictive Modeling of Multiplex Chemical Phenomics for Novel Cells and Patients: Applied to Personalized Alzheimer's Disease Drug Repurposing

abstract: Chemical phenomics which measures multiplex chemical-induced phenotypic response of cells or patients, particularly dose-dependent transcriptomics and drug-response curves, provides new opportunities for in silico mechanism-driven phenotype-based drug discovery. However, state-of-the-art computational methods only focus on predicting a single phenotypic readout and are less successful in screening compounds for novel cells or individual patients. We designed a new deep learning model, MultiDCP, to enable high-throughput compound screening based on multiplex chemical phenomics for the first time, and further expand the scope of chemical phenomics to unexplored cells and patients. The novelties of MultiDCP lie in a multi-task learning framework with a novel knowledge-driven autoencoder to integrate incoherent labeled and unlabeled omics data, and a teacher-student training strategy to exploit unreliable data. MultiDCP significantly outperforms the state-of-the-art for novel cell lines. The predicted chemical transcriptomics demonstrate a stronger predictive power than noisy experimental data for downstream tasks. We applied MultiDCP to repurpose individualized drugs for Alzheimers disease, suggesting that MultiDCP is a potentially powerful tool for personalized medicine.


title: H3-K27M-Mutant Nucleosomes Interact with MLL1 to Shape the Glioma Epigenetic Landscape

abstract: Cancer-associated mutations in genes encoding histones dramatically reshape chromatin and support tumorigenesis. Lysine to methionine substitution of residue 27 on histone H3 (K27M) is a driver mutation in high-grade pediatric gliomas, known to abrogate Polycomb Repressive Complex 2 (PRC2) activity. We applied single-molecule systems to image individual nucleosomes and delineate the combinatorial epigenetic patterns associated with H3-K27M expression. We found that chromatin marks on H3-K27M-mutant nucleosomes are dictated both by their incorporation preferences and by intrinsic properties of the mutation. Mutant nucleosomes not only preferentially bind PRC2, but also directly interact with MLL1, thus leading to genome-wide redistribution of H3K4me3. H3-K27M-mediated deregulation of both repressive and active chromatin marks leads to unbalanced  bivalent chromatin, which may support a poorly differentiated cellular state. This study provides evidence for a direct effect of H3-K27M oncohistone on the MLL1-H3K4me3 pathway and highlights the capability of single-molecule tools to reveal mechanisms of chromatin deregulation in cancer.


title: Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework

abstract:
BackgroundMass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens. This allows for spatial characterization of molecular compositions of different tissue types and tumor subtypes, which bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of an urothelial carcinoma dataset.

MethodsTryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle invasive urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list.

ResultsRigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle invasive tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: Stromal tissue was characterized by collagen type I peptides and tumor tissue by histone and heat shock protein beta-1 peptides.

Intermediate filaments such as cytokeratins and vimentin were discriminative between the tumors with different muscle-infiltration status. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links.

ConclusionHere, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.


title: Dose-dependent dissociation of pro-cognitive effects of donepezil on attention and cognitive flexibility in rhesus monkeys

abstract:
BACKGROUNDDonepezil exerts pro-cognitive effects by non-selectively enhancing acetylcholine (ACh) across multiple brain systems. The brain systems that mediate pro-cognitive effects of attentional control and cognitive flexibility are the prefrontal cortex and the anterior striatum which have different pharmacokinetic sensitivities to ACh modulation. We speculated that these area-specific ACh profiles lead to distinct optimal dose-ranges for donepezil to enhance the cognitive domains of attention and flexible learning.

METHODSTo test for dose-specific effects of donepezil on different cognitive domains we devised a multi-task paradigm for nonhuman primates (NHPs) that assessed attention and cognitive flexibility. NHPs received either vehicle or variable doses of donepezil prior to task performance. We measured donepezil intracerebral and how strong it prevented the breakdown of ACh within prefrontal cortex and anterior striatum using solid-phase-microextraction neurochemistry.

RESULTSThe highest administered donepezil dose improved attention and made subjects more robust against distractor interference, but it did not improve flexible learning. In contrast, only a lower dose range of donepezil improved flexible learning and reduced perseveration, but without distractor-dependent attentional improvement. Neurochemical measurements confirmed a dose-dependent increase of extracellular donepezil and decreases in choline within the prefrontal cortex and the striatum.

CONCLUSIONSThe donepezil dose for maximally improving attention functions differed from the dose range that enhanced cognitive flexibility despite the availability of the drug in the major brain systems supporting these cognitive functions. Thus, the non-selective acetylcholine esterase inhibitor donepezil inherently trades improvement in the attention domain for improvement in the cognitive flexibility domain at a given dose range.

category: pharmacology and toxicology


title: Engineering site-selective incorporation of fluorine into natural product analogs

abstract: While bioactive compounds are commonly derived both by human design as well as from living organisms, man-made and natural products typically display very different structural characteristics. As such, a longstanding goal in the discovery of new molecular function is to develop approaches to incorporate the advantageous elements of both groups of molecules, thereby expanding the molecular space accessible for this purpose. In this work, we report the engineering a fluorine-selective enzyme that can complement mutated acyltransferase (AT) domains of a modular polyketide synthase, which are the main determinants of the identity and location of substituents on polyketides, to produce different fluorinated regioisomers of the erythromycin precursor in vitro. We further show that by engineering cell uptake of fluorinated building blocks, we can control fluorine selectivity in vivo to produce selectively fluorinated polyketides using engineered E. coli. These results demonstrate that it is possible to introduce fluorine, a key synthetic design element for drug development, selectively into the scaffold of a complex natural product and produce these analogs by microbial fermentation.

category: synthetic biology


title: DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

abstract:
Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Author SummaryTime-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time. Deep learning tools are very useful for analyzing these data and can identify the location of cells and track their position over time. In this work, we introduce a new version of our Deep Learning for Time-lapse Analysis (DeLTA) software, which includes the ability to robustly segment and track bacteria that are growing in two dimensions, such as on agarose pads or within microfluidic environments. This capability is essential for experiments where spatial and positional effects are important, such as conditions with microbial co-cultures, cell-to-cell interactions, or spatial patterning. The software also tracks pole age and can be used to analyze replicative aging. These new features join other improvements, such as the ability to work directly with many common microscope file formats. DeLTA 2.0 can reliably track hundreds of cells with low error rates, making it an ideal tool for high throughput analysis of microscopy data.

category: systems biology


title: Trial by trial, machine learning approach identifies temporally discrete Aδ- and C-fibre mediated laser evoked potentials that predict pain behaviour in rats.

abstract: Laser evoked potentials (LEPs) - the EEG response to temporally-discrete thermal stimuli - are commonly used in experimental pain studies in humans. Such stimuli selectively activate nociceptors and produce EEG features which correlate with pain intensity. The rodent LEP has been proposed to be a translational biomarker of nociception and pain, however its validity has been questioned because of reported differences in the classes of nociceptive fibres mediating the response. Here we use a machine learning, trial by trial analysis approach on wavelet-denoised LEPs generated by stimulation of the plantar hindpaw of rats. The LEP amplitude was more strongly related to behavioural response than to laser stimulus energy. A simple decision tree classifier using LEP features was able to predict behavioural responses with 73% accuracy. An examination of the features used by the classifier showed that mutually exclusive short and long latency LEP peaks were clearly seen in single-trial data, yet were not evident in grand average data pooled from multiple trials. This bimodal distribution of LEP latencies was mirrored in the paw withdrawal latencies which were preceded and predicted by the LEP responses. The proportion of short latency events was increased after intradermal application of high dose capsaicin (to defunctionalise TRPV1 expressing nociceptors), suggesting they were mediated by A{delta}-fibres (specifically AMH-I). These findings demonstrate that both C- and A{delta}-fibres contribute to rodent LEPs and concomitant behavioural responses, providing a real-time assay of specific fibre function in conscious animals. Single-trial analysis approaches can improve the utility of LEPs as a translatable biomarker of pain.


title: Information theory-based approach towards studying anti-coincidence detection via graded amplitude dendritic action potentials.

abstract:
In contrast to typical all or none action potential, recent discovery of graded amplitude action potentials in cortical neurons enabled the dendrites to perform XOR computation, previously thought to be performed only at network level. Thus, these special neurons can perform anti-coincidence detection at the dendritic level, but a lot is unanswered about this phenomenon. Can such experimentally observed dendritic action potential generating system transmit information about stimuli having varying degrees of temporal overlap? Can the system add to the repertoire of computations performed at dendritic level by enhancing the information transmission about varying amplitude stimuli? In this information theory-based study done in single compartment and two-compartment dendritic models, it is shown that such a system can indeed transmit information about the temporal overlap of stimuli as well as amplitudes of stimuli even at high input noise levels. First, the calculation of mutual information between single stimulus and response i.e. I(S;R) with varying noise showed that the information about temporally overlapping nature of stimuli is precisely transmitted by such a system. Secondly, the time evolution of mutual information was simulated through data from the system and it positively reinforced the above-mentioned result. Next, varying amplitude input stimuli was provided to the system and calculation of mutual information between two stimuli and one response i.e. I(S1,S2;R) with varying noise levels revealed that such a system optimally transmits the information about stimuli even at high noise levels. Finally, calculation of this information measurement with respect to time in an experiment with constant overlap but varying input amplitude again positively reinforced the result.

Key PointsO_LIInformation theory-based measurements were employed to assess the role of graded amplitude dendritic action potentials.
C_LIO_LIAction potentials (APs) with maximal amplitudes for threshold level stimuli and lower amplitudes for stronger stimuli were modelled with high voltage Ca2+ (HVA like) channels, Ca2+ dependent (BK-like) channel, leak channel and calcium pump in a single compartment model and two compartment dendritic model.
C_LIO_LIAnalysis done here, on comparison with control compartment generating constant amplitude AP via standard Hodgkin-Huxley sodium potassium channel revealed that such a compartment shows optimal information transmission about both varying amplitudes input current stimuli as well as varying time overlap stimuli.
C_LI


title: Spectral inference reveals principal cone-integration rules of the zebrafish inner retina

abstract:
In the vertebrate retina, bipolar cells integrate the signals from different cone types at two main sites: directly, via dendritic inputs in the outer retina, and indirectly, via axonal inputs in the inner retina. Of these, the functional wiring of the indirect route, involving diverse amacrine cell circuits, remains largely uncharted. However, because cone-photoreceptor types differ in their spectral sensitivities, insights into the total functional cone-integration logic of bipolar cell might be gained by linking spectral responses across these two populations of neurons. To explore the feasibility of such a "spectral-circuit-mapping" approach, we here recorded in vivo responses of bipolar cell presynaptic terminals in larval zebrafish to widefield but spectrally resolved flashes of light. We then mapped the results onto the previously established spectral sensitivity functions of the four cones.

We find that this approach could explain [~]95% of the spectral and temporal variance of bipolar cell responses by way of a simple linear model that combined weighted inputs from the cones with four stereotyped temporal components. This in turn revealed several notable integration rules of the inner retina. Overall, bipolar cells were dominated by red-cone inputs, often alongside equal sign inputs from blue- and green-cones. In contrast, UV-cone inputs were uncorrelated with those of the remaining cones. This led to a new axis of spectral opponency which was mainly set-up by red-/green-/blue-cone "Off" circuits connecting to "natively-On" UV-cone circuits in the outermost fraction of the inner plexiform layer - much as how key colour opponent circuits are established in mammals. Beyond this, and despite substantial temporal diversity that was not present in the cones, bipolar cell spectral tunings were surprisingly simple. They either approximately resembled both opponent and non-opponent spectral motifs already present in the cones or exhibited a stereotyped non-opponent broadband response. In this way, bipolar cells not only preserved the efficient spectral representations in the cones, but also diversified them to set up a total of six dominant spectral motifs which included three axes of spectral opponency. More generally, our results contribute to an emerging understanding of how retinal circuits for colour vision in ancestral cone-tetrachromats such as zebrafish may be linked to those found in mammals.


title: Whole-brain connectivity during encoding: age-related differences and associations with cognitive and brain structural decline

abstract: There is a limited understanding of age differences in functional connectivity during memory encoding. In the present study, a sample of cognitively healthy adult participants (n=488), a subsample of whom had longitudinal cognitive and brain structural data spanning 8 years back, underwent fMRI while performing an associative memory encoding task. We investigated 1) age changes in whole-brain connectivity during memory encoding; whether 2) encoding connectivity patterns overlap with the activity signatures of specific cognitive processes and whether 3) connectivity changes associated with memory encoding related to longitudinal brain structural and cognitive changes. Age was associated with decreased intranetwork connectivity and increased connectivity during encoding. Task-connectivity between mediotemporal and posterior parietal regions - which overlapped with areas involved in mental imagery - was related to better memory performance only in older age. The connectivity patterns supporting memory performance in older age reflected preservation of thickness of the medial temporal cortex. These investigations collectively indicate that functional patterns of connectivity should be interpreted in accordance with a maintenance rather than a compensation account.


title: ACE2 Pathway Regulates Thermogenesis and Energy Metabolism

abstract: Identification of key regulators of energy homeostasis holds important therapeutic promise for metabolic disorders, such as obesity and diabetes. ACE2 cleaves angiotensin II (Ang II) to generate Ang-(1-7) which acts mainly through the Mas receptor. Here, we identify ACE2 pathway as a critical regulator in the maintenance of thermogenesis and energy expenditure. We found that ACE2 is highly expressed in brown adipose tissue (BAT) and that cold stimulation increases ACE2 and Ang-(1-7) levels in BAT and serum. ACE2 knockout mice (ACE2-/y), Mas knockout mice (Mas-/-), and the mice transplanted with brown adipose tissue from Mas-/- mice displayed impaired thermogenesis. In contrast, impaired thermogenesis of db/db obese diabetic mice and high-fat diet-induced obese mice were ameliorated by overexpression of ACE2 or continuous infusion of Ang-(1-7). Activation of ACE2 pathway was associated with improvement of metabolic parameters, including blood glucose, lipids and energy expenditure in multiple animal models. Consistently, ACE2 pathway remarkably enhanced the browning of white adipose tissue. Mechanistically, we showed that ACE2 pathway activated Akt/FoxO1 and PKA pathway, leading to induction of UCP1 and activation of mitochondrial function. Our data propose that adaptive thermogenesis requires regulation of ACE2 pathway and highlight novel therapeutic targets for the treatment of metabolic disorders.


title: Arap1 Loss Causes RPE Phagocytic Dysfunction and Subsequent Photoreceptor Death

abstract:
PurposeArap1 is an Arf-directed GTPase-activating protein (GAP) shown to modulate actin cytoskeletal dynamics by regulating Arf and Rho family members. We have previously shown that Arap1-/- mice develop photoreceptor degeneration similar to the human condition retinitis pigmentosa (RP), corroborated by fundus examination, histopathology, and ERG analysis. However, Arap1 expression was not detected in photoreceptors, but in Muller Glia and retinal pigment epithelium (RPE), suggesting a non-cell-autonomous mechanism for degeneration. The aim of this study was to elucidate the role of retinal Arap1 in photoreceptor maintenance.

MethodsAlbino Arap1-/- mice were generated via breeding pigmented Arap1-/- mice onto a Tyr-/- C57BL/6J background. Conditional knockout (cKO) mice were generated for Muller Glia/RPE, Muller Glia, and RPE via targeting Cralbp, Glast, and Vmd2 promoters, respectively, to drive Cre recombinase expression to knock out Arap1. Mice were analyzed by fundus photography, optical coherence tomography (OCT), histology, and immunohistochemistry. Arap1 binding partners were assayed by affinity purification mass spectrometry.

ResultsVmd2-Cre Arap1tm1c/tm1c and Cralbp-Cre Arap1tm1c/tm1c mice, but not Glast-Cre Arap1tm1c/tm1c mice, recapitulated the photoreceptor degeneration phenotype originally observed in germline Arap1-/- mice. These findings were corroborated by fundus exam, OCT, and histological analysis. Mass spectrometry analysis of ARAP1 co-immunoprecipitation identified putative binding partners of ARAP1, revealing numerous interactants involved in phagocytosis, cytoskeletal composition, intracellular trafficking, and endocytosis. Quantification of rod outer segment (OS) phagocytosis in vivo demonstrated a clear phagocytic defect in Arap1-/- mice compared to Arap1+/+ littermate controls while cone phagocytosis was preserved.

ConclusionsArap1 expression, specifically in RPE, is necessary for photoreceptor survival due to its indispensable function in RPE phagocytosis. We propose a model in which Arap1 regulates G-protein function for nonmuscle myosin II targeting during phagocytosis. This novel role of Arap1 is important for further understanding of both the diversity of its functions and the complex molecular regulation of RPE phagocytosis.


title: Retinoids rescue ceruloplasmin secretion and alleviate oxidative stress in Wilson's disease-specific hepatocytes

abstract: Wilsons disease (WD) is a copper metabolic disorder, which is caused by defective ATP7B function. Here, we have generated induced pluripotent stem cells (iPSCs) from WD patients carrying compound heterozygous mutations on ATP7B. ATP7B loss- and gain-of-functions were further manifested with ATP7B-deficient iPSCs and heterozygously-corrected R778L WD patient-derived iPSCs using CRISPR-Cas9-based gene editing. Transcriptome analysis identified abnormalities of retinoid signaling pathway and lipid metabolism in WD-specific hepatocytes. Although the expression level of ATP7B protein was variable among WD-specific hepatocytes, the expression and secretion of ceruloplasmin (Cp), which is a downstream copper carrier in plasma, were consistently decreased. Cp secretion-based drug screening identified all-trans retinoic acid (ATRA) as promising candidates for rescuing Cp secretion. ATRA also alleviated reactive oxygen species (ROS) production induced by lipid accumulation in WD-specific hepatocytes. Our patient-derived iPSC-based hepatic models provide potential therapeutics for liver steatosis in WD and other fatty liver diseases.


title: Stable Flow-induced Expression of KLK10 Inhibits Endothelial Inflammation and Atherosclerosis

abstract:
IntroductionAtherosclerosis preferentially occurs in arterial regions exposed to disturbed blood flow (d-flow), while regions exposed to stable flow (s-flow) are protected. The proatherogenic and atheroprotective effects of d-flow and s-flow are mediated in part by the global changes in endothelial cell gene expression, which regulates endothelial dysfunction, inflammation, and atherosclerosis. Previously, we identified Kallikrein-Related Peptidase 10 (KLK10, a secreted serine protease) as a flow-sensitive gene in arterial endothelial cells, but its role in endothelial biology and atherosclerosis was unknown.

Methods and ResultsHere, we show that KLK10 is upregulated under s-flow conditions and downregulated under d-flow conditions using in vivo mouse models and in vitro studies with cultured endothelial cells (ECs). Single-cell RNA sequencing (scRNAseq) and scATAC sequencing (scATACseq) study using the partial carotid ligation mouse model showed flow-regulated KLK10 expression at the epigenomic and transcription levels. Functionally, KLK10 protected against d-flow-induced inflammation and permeability dysfunction in human artery ECs (HAECs). Further, treatment of mice in vivo with rKLK10 decreased arterial endothelial inflammation in d-flow regions. Additionally, rKLK10 injection or ultrasound-mediated transfection of KLK10-expressing plasmids inhibited atherosclerosis in ApoE-/- mice. Studies using pharmacological inhibitors and siRNAs revealed that the anti-inflammatory effects of KLK10 were mediated by a Protease Activated Receptors (PAR1/2)-dependent manner. However, unexpectedly, KLK10 did not cleave the PARs. Through a proteomics study, we identified HTRA1 (High-temperature requirement A serine peptidase 1), which bound and cleaved KLK10. Further, siRNA knockdown of HTRA1 prevented KLK10s anti-inflammatory and barrier protective function in HAECs, suggesting that HTRA1 regulates KLK10 function. Moreover, KLK10 expression was significantly reduced in human coronary arteries with advanced atherosclerotic plaques compared to those with less severe plaques.

ConclusionKLK10 is a flow-sensitive endothelial protein and, in collaboration with HTRA1, serves as an anti-inflammatory, barrier-protective, and anti-atherogenic factor.


title: Generation of iPSCs from endangered Grevy's zebra and comparative transcriptomic analysis of mammalian PSCs

abstract: Induced pluripotent stem cells (iPSCs) can provide a biological resource for functional and conservation research in various species. This expectation has led to generation of iPSCs from various species, including those identified as endangered species. However, the understanding of species variation in mammalian iPSCs is largely unknown. Here, to gain insight into the species variation in iPSCs, we the first generated iPSCs from the endangered species Grevys zebra (Equus grevyi; gz-iPSCs) for the first time in the world. We isolated primary fibroblasts cell from an individual that had died of natural causes at a zoo and reprogrammed the fibroblasts into iPSCs. We confirmed their pluripotency and differentiation potential and performed RNA sequencing analysis. The gz-iPSC transcriptome showed that the generated gz-iPSCs robustly expressed genes associated with pluripotency and reprogramming processes, including epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions. Comparative transcriptomics with other species revealed patterns of gene expression among mammalian PSCs and detected evolutionary conservation of pluripotency-associated genes and the plausible importance of the translation process. This study provides new insights into the evolution of mammalian PSCs, and the species conservation and variation of PSCs will advance our understanding of the early development of mammals.


title: Mammal virus diversity estimates are unstable due to accelerating discovery effort

abstract: Host-virus association data form the backbone of research into eco-evolutionary drivers of viral diversity and host-level zoonotic risk. However, knowledge of the wildlife virome is inherently constrained by historical discovery effort, and there are concerns that the reliability of ecological inference from host-virus data may be undermined by taxonomic and geographical sampling biases. Here, we evaluate whether current estimates of host-level viral diversity in wild mammals are stable enough to be considered biologically meaningful, by analysing a comprehensive dataset of discovery dates of 6,571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are still either constant or accelerating, with little evidence of declines towards viral richness asymptotes in even highly-sampled hosts. Consequently, inference of relative viral richness across host species has been unstable over time, particularly in bats, where intensified surveillance since the early 2000s caused a rapid rearrangement of species ranked viral richness. Our results show that comparative inference of host-level virus diversity across mammals is highly sensitive to even short-term changes in sampling effort. We advise caution to avoid overinterpreting patterns in current data, since our findings suggest that an analysis conducted today could feasibly draw quite different conclusions than one conducted only a decade ago.


title: Phylogenetic analysis of TALE superclass homeobox genes in amphipod crustaceans

abstract: TALE class genes are a group of developmentally conserved transcription factors found in animals. Here, we describe the identification and phylogenetic analysis of TALE class genes in amphipod crustaceans. We identified 241 putative TALE class genes from 56 amphipod crustacean species. Phylogenetic analysis of the genes revealed six subclasses. We provide a list of FASTA sequences of the genes identified. Results from this work may inform future evolutionary and comparative genomics studies on animal development.

category: evolutionary biology


title: Analysis of sub-kilobase chromatin topology reveals nano-scale regulatory interactions with variable dependence on cohesin and CTCF

abstract: Enhancers and promoters predominantly interact within large-scale topologically associating domains (TADs), which are formed by loop extrusion mediated by cohesin and CTCF. However, it is unclear whether complex chromatin structures exist at sub-kilobase-scale and to what extent fine-scale regulatory interactions depend on loop extrusion. To address these questions, we present an MNase-based chromosome conformation capture (3C) approach, which has enabled us to generate the most detailed local interaction data to date and precisely investigate the effects of cohesin and CTCF depletion on chromatin architecture. Our data reveal that cis-regulatory elements have distinct internal nano-scale structures, within which local insulation is dependent on CTCF, but which are independent of cohesin. In contrast, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.


title: Single-molecule simultaneous profiling of DNA methylation and DNA-protein interactions with Nanopore-DamID

abstract: DNA-protein interactions and cytosine methylation control eukaryotic gene expression. Here, we present an approach to simultaneously detect cytosine methylation and DNA-protein interactions from single molecules, through selective sequencing of adenine-labelled DNA. Applying this approach to LaminB1-associated heterochromatin domains, we identify strict CpG methylation maintenance at transcriptional start sites amidst a generalised relaxation of methylation, potentially to prevent ectopic and aberrant heterochromatic gene expression.


title: Echolocating whales use ultra-fast echo-kinetic responses to track evasive prey.

abstract: Visual predators rely on fast-acting optokinetic reflexes to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pursuits. If echoes are processed on a click-by-click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high-resolution bio-logging of wild predator-prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50-200ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo-kinetic responses from trained harbour porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro-kinetic response times in echolocation are similar to those of tracking reflexes in vision, suggesting a common neural underpinning.

category: animal behavior and cognition


title: pOPIN-GG: A resource for modular assembly in protein expression vectors

abstract:
The ability to recombinantly produce target proteins is essential to many biochemical, structural, and biophysical assays that allow for interrogation of molecular mechanisms behind protein function. Purification and solubility tags are routinely used to maximise the yield and ease of protein expression and purification from E. coli. A major hurdle in high-throughput protein expression trials is the cloning required to produce multiple constructs with different solubility tags. Here we report a modification of the well-established pOPIN expression vector suite to be compatible with modular cloning via Type IIS restriction enzymes. This allows users to rapidly generate multiple constructs with any desired tag, introducing modularity in the system and delivering compatibility with other modular cloning vector systems, for example streamlining the process of moving between expression hosts. We demonstrate these constructs maintain the expression capability of the original pOPIN vector suite and can also be used to efficiently express and purify protein complexes, making these vectors an excellent resource for high-throughput protein expression trials.

HighlightsO_LIpOPIN-GG expression vectors allow for modular cloning enabling rapid screening of purification and solubility tags at no loss of expression compared to previous vectors.
C_LIO_LICloning into the pOPIN-GG vectors can be performed from PCR products or from level 0 vectors containing the required parts.
C_LIO_LISeveral vectors with different resistances and origins of replication have been generated allowing the effective co-expression and purification of protein complexes.
C_LIO_LIAll pOPIN-GG vectors generated here are available on Addgene, as well as level 0 acceptors and tags.
C_LI


title: A computational approach for generating smooth estimates of motor unit discharge rates and visualizing population discharge characteristics

abstract:
ObjectiveSuccessive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize.

ApproachIn the present study, we propose support vector regression (SVR) as an improved approach for generating continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions.

Main ResultCompared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization.

SignificanceThe results and methods outlined here provide an improved method for generating smooth estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

category: bioengineering


title: geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.

abstract: The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package (https://github.com/MarioniLab/geneBasisR).

category: bioinformatics


title: Estimating Intraclonal Heterogeneity and Subpopulation Changes from Perturbational Bulk Gene Expression Profiles in LINCS L1000 CMap by Premnas

abstract:
BackgroundThe connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases.

MotivationHowever, due to the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections.

ResultsIn this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of searching for drug cocktails and drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined.

ConclusionsThe proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology. The executable and source code of Premnas is freely available at https://github.com/jhhung/Premnas.

HighlightsO_LIA computational framework (i.e., Premnas) for learning subpopulation characteristics by the archetypal analysis and deconvoluting bulk expression profiles using the digital cytometry into subpopulation composition was proposed and validated.
C_LIO_LIIntraclonal heterogeneity and subpopulation changes upon different perturbagen treatments in LINCS CMap L1000 datasets were estimated by Premnas.
C_LIO_LIWith Premnas, we introduced a new strategy of finding effective drug cocktails and further linked the drug-resistant subpopulation found in CMap L1000 to a known drug-resistant clone (i.e., the pre-adapted [PA] cell).
C_LIO_LITo our best knowledge, this work is the first attempt to provide a new subpopulation perspective to CMap database.
C_LIO_LIWe believe Premnas can be applied to all the perturbation datasets, of which intraclonal/intratumoral heterogeneity was concealed by the bulk profiling and hereafter provides a new dimension of interpreting the connectivity.
C_LI


title: MiSDEED: a synthetic multi-omics engine for microbiome power analysis and study design

abstract:
SummaryMiSDEED is a command-line tool for generating synthetic longitudinal multi-omics data from simulated microbial environments. It generates relative-abundance timecourses under perturbations for an arbitrary number of samples and patients. All simulation parameters are exposed to the user to facilitate rapid power analysis and aid in study design. Users who want additional flexibility may also use MiSDEED as a Python package.

Availability and implementationMiSDEED is written in Python and is freely available at https://github.com/pchlenski/misdeed.

Contactpac@cs.columbia.edu


title: Assessment of 3D MINFLUX data for quantitative structural biology in cells

abstract: MINFLUX is a promising new development in single-molecule localization microscopy, claiming a resolution of 1-3 nm in living and fixed biological specimens. While MINFLUX can achieve very high localisation precision, quantitative analysis of reported results leads us to dispute the resolution claim and question reliability for imaging sub-100-nm structural features, in its current state.

category: biophysics


title: MYCN mediates cysteine addiction and sensitizes to ferroptosis

abstract: Aberrant expression of MYC family members predicts poor clinical outcome in many human cancers. Oncogenic MYC profoundly alters metabolism and mediates an antioxidant response to maintain redox balance. Here we show that MYC induces massive lipid peroxidation upon depletion of cysteine, the rate-limiting amino acid for glutathione biosynthesis and sensitizes cells to ferroptosis, an oxidative, non-apoptotic and irondependent type of cell death. In MYCN-amplified childhood neuroblastoma, MYCN mediates resistance to ferroptosis by activating transsulfuration of methionine to cysteine. MYCN may contribute to spontaneous tumor regression in low-risk neuroblastomas by promoting ferroptosis in cells with epigenetically silenced cystathionine-beta-synthase, the rate-limiting enzyme for transsulfuration. We identified enzymes and antiporter proteins crucial to ferroptotic escape, providing multiple previously unknown sites that may be acted on therapeutically.


title: Laterally transferred macrophage mitochondria acts as a signaling source promoting cancer cell proliferation

abstract:
Lateral transfer of mitochondria occurs in many physiological and pathological conditions. Given that mitochondria provide essential energy for cellular activities, mitochondrial transfer is currently thought to promote the rescue of damaged cells. We report that mitochondrial transfer occurs between macrophages and breast cancer cells, leading to increased cancer cell proliferation. Unexpectedly, transferred macrophage mitochondria are dysfunctional, lacking mitochondrial membrane potential. Rather than performing essential mitochondrial activities, transferred mitochondria accumulate reactive oxygen species which activates ERK signaling, indicating that transferred mitochondria act as a signaling source that promotes cancer cell proliferation. We also demonstrate that pro-tumorigenic M2-like macrophages exhibit increased mitochondrial transfer to cancer cells. Collectively, our findings reveal how mitochondrial transfer is regulated and leads to sustained functional changes in recipient cells.

One-Sentence SummaryLateral transfer of macrophage mitochondria acts as a ROS signaling source, regulating cancer cell proliferation through ERK signaling.


title: Clonal reconstruction from co-occurrence of vector integration sites allows accurate quantification of expanding clones in vivo

abstract:
High transduction rates of viral vectors in gene therapies (GT) and experimental hematopoiesis ensure a high frequency of gene delivery, although multiple integration events can occur in the same cell. Therefore, tracing of integration sites (IS) leads to misquantification of the true clonal spectrum and limits safety considerations in GT.

Hence, we use correlations between repeated measurements of IS abundances to estimate their mutual similarity and identify clusters of co-occurring IS, for which we assume a clonal origin. We evaluate the performance, robustness and specificity of our methodology using clonal simulations. The reconstruction methods, implemented and provided as an R-package, are further applied to experimental clonal mixes and to a preclinical model of hematopoietic GT.

Our results demonstrate that clonal reconstruction from IS data allows to overcome systematic biases in the clonal quantification as an essential prerequisite for the assessment of safety and long-term efficacy of GT involving integrative vectors.


title: Multiomics reveals the genomic, proteomic and metabolic influences of histidyl dipeptides on heart

abstract: Histidyl dipeptides, are synthesized in the heart via enzyme carnosine synthase (Carns), which facilitates glycolysis and glucose oxidation by proton buffering and attenuate ischemia and reperfusion injury. However, a composite understanding of the histidyl dipeptide mediated responses in the heart are lacking. We performed multilayer omics in the cardio specific Carns overexpressing mice, showing higher myocardial levels of histidyl dipeptides lead to extensive changes in microRNAs that could target the expression of contractile proteins and enzymes involved in {beta}-fatty acid oxidation and citric acid cycle (TCA). Similarly, global proteomics showed contractile function, fatty acid degradation and TCA cycle, pathways were enriched in the CarnsTg heart. Parallel with these changes, free fatty acids, and TCA intermediate-succinic acid were lower under aerobic and significantly attenuated under anaerobic conditions in the CarnsTg heart. Integration of multiomics data shows {beta}-fatty acid oxidation and TCA cycle exhibit correlative changes at all three levels in CarnsTg heart, suggesting histidyl dipeptides are critical regulators of myocardial structure, function and energetics.


title: Vision is Required for Cell Type Specification in the Visual Cortex

abstract: The role of postnatal experience in sculpting cortical circuitry, while long appreciated, is poorly understood at the level of cell types. We explore this in the mouse primary visual cortex (V1) using single-nucleus RNA-sequencing, visual deprivation, genetics, and functional imaging. We find that vision selectively drives the specification of glutamatergic cell types in upper layers (L) (L2/3/4), while deeper-layer glutamatergic, GABAergic, and non-neuronal cell types are established prior to eye opening. L2/3 cell types form an experience-dependent spatial continuum defined by the graded expression of [~]200 genes, including regulators of cell adhesion and synapse formation. Vision-dependent regulation of one of these genes, encoding the inhibitory synaptic cell adhesion molecule IGSF9b, is required for the normal development of binocular responses in L2/3. In summary, vision preferentially regulates the development of upper-layer glutamatergic cell types through the regulation of cell type-specific gene expression programs.

category: neuroscience


title: Age and sex dependent variability of type 2 dopamine receptors in the human brain: A large-scale PET cohort

abstract:
BACKGROUNDThe dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved.

METHODSIn this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018.

RESULTSStriatal D2R availability decreased through age for both sexes and was higher in females versus males throughout age. BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135).

CONCLUSIONSD2R density is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.


title: Development of motor neurons and motor activity in zebrafish requires F-actin nucleation by Fmn2b

abstract:
BackgroundCytoskeletal remodelling plays a pivotal role in the establishment of neuronal connectivity during development and in plasticity in adults. Mutations in the cytoskeleton regulatory protein Formin-2 (Fmn2) are associated with neurodevelopmental disorders like intellectual disability, though its function in neuronal morphogenesis has not been characterised in vivo.

ResultsHere we develop a loss-of-function model for fmn2b, the zebrafish orthologue of Fmn2, using CRISPR/Cas9-mediated gene editing. fmn2b mutants display motor deficits starting from the earliest motor responses in the embryo. We find that fmn2b is expressed in spinal motor neurons and its loss reduces motor neuron innervation of the axial muscles without affecting myotome integrity. The translocation of caudal primary (CaP) motor neuron outgrowth is compromised in fmn2b mutants, while rostral primary (RoP) motor neurons have missing soma or stall at the horizontal myoseptum. Strikingly, axon collateral branching of the motor neurons is severely compromised and results in reduced synaptic coverage of the myotome. Rescue experiments identify the requirement for Fmn2-mediated actin nucleation for motor neuron outgrowth and arborisation.

ConclusionsThe zebrafish loss-of-function model of Fmn2 reveals the specific requirement of F-actin polymerisation by Fmn2 in neuromuscular development. It also underscores the role of Fmn2 in motor neuropathies, especially as a proportion of individuals harbouring mutations in Fmn2 present with hypotonia.


title: Reservoir host studies of Lloviu virus: first isolation, sequencing and serology in Schreiber's bats in Europe

abstract:
IntroductoryFiloviruses are prime examples of emerging human pathogens that are transmitted to humans by zoonotic spillover events. Since their initial discovery, filovirus outbreaks have occured with increasing frequency and intensity. There is an urgent need to better understand their enzootic ecology and pathogenic potential, given recent zoonotic virus spillover events including the 2013-2016 West African Ebola virus (EBOV) epidemic. Several novel filoviruses have been discovered with a markedly wider geographic distribution than previously described. One of these novel filoviruses, Lloviu virus (LLOV), was first identified in 2002 in Schreibers bats (Miniopterus schreibersii) in Spain, Portugal, and southern France. Subsequently, in 2016, LLOV was detected during the passive monitoring of bats in Hungary.

Here we report the first isolation of infectious Lloviu virus; from the blood of an asymptomatic Schreibers bat, subsequently cultivated in the Miniopterus sp. kidney cell line SuBK12-08. We also show that LLOV is able to infect monkey and human cells, suggesting that LLOV might have spillover potential. We performed a multi-year surveillance of LLOV and detected LLOV RNA in both deceased and asymptomatic live animals as well as in coupled ectoparasites from the families Nycteribiidae and Ixodidae. We present data on the natural infection and seropositivity of Schreibers bats and provide novel LLOV genomic sequence information from a bat host and an arthropod parasite. Our data support the role of bats, specifically Miniopterus schreibersii as natural reservoirs for the LLOV filovirus in Europe. We also suggest that bat-associated parasites might play a role in the natural ecology of filoviruses in temperate climate regions compared to filoviruses in the tropics. These results raise the possibility of cross-species transmission events via bat-human interactions.

category: microbiology


title: Mucin induces CRISPR-Cas defence in an opportunistic pathogen

abstract: Parasitism by bacteriophages has led to the evolution of a variety of defense mechanisms in their host bacteria. However, it is unclear what factors lead to specific defenses being deployed upon phage infection. To explore this question, we exposed the bacterial fish pathogen Flavobacterium columnare to its virulent phage V156 in the presence of a eukaryotic host signal (mucin). All tested conditions led to some level of innate immunity, but the presence of mucin led to a dramatic increase in CRISPR spacer acquisition, especially in low nutrient conditions where over 60% of colonies had obtained at least one new spacer. Additionally, we show that the presence of a competitor bacterium further increases CRISPR spacer acquisition in F. columnare. These results suggest that ecological factors are important in determining defense strategies against phages, and that the concentration of phages on metazoan surfaces may select for the diversification of bacterial immune systems.