title: A computational approach to evaluate the combined effect of SARS-CoV-2 RBD mutations and ACE2 receptor genetic variants on infectivity: The COVID-19 host-pathogen nexus

abstract: SARS-CoV-2 infectivity is largely determined by the virus Spike protein binding to the ACE2 receptor. Meanwhile, marked infection rate differences were reported between populations and individuals. To understand the disease dynamic, we developed a computational approach to study the implications of both SARS-CoV-2 RBD mutations and ACE2 polymorphism on the stability of the virus-receptor complex. We used the 6LZG PDB RBD/ACE2 3D model, the mCSM platform, the LigPlot+ and PyMol software to analyze the data on SARS-CoV-2 mutations and ACE variants retrieved from GISAID and Ensembl/GnomAD repository. We observed that out of 351 RBD point mutations, 83% destabilizes the complex according to free energy ({Delta}{Delta}G) differences. We also spotted variations in the patterns of polar and hydrophobic interactions between the mutations occurring in 15 out of 18 contact residues. Similarly, comparison of the effect on the complex stability of different ACE2 variants showed that the pattern of molecular interactions and the complex stability varies also according to ACE2 polymorphism. We infer that it is important to consider both ACE2 variants and circulating SARS-CoV-2 RBD mutations to assess the stability of the virus-receptor association and evaluate infectivity. This approach might offers a good molecular ground to mitigate the virus spreading.


title: Cortical Responses to the Amplitude Envelopes of Sounds Change with Age

abstract:
Many older listeners have difficulty understanding speech in noise, when cues to speech-sound identity are less redundant. The amplitude envelope of speech fluctuates dramatically over time, and features such as the rate of amplitude change at onsets (attack) and offsets (decay) signal critical information about the identity of speech sounds. Aging is also thought to be accompanied by increases in cortical excitability, which may differentially alter sensitivity to envelope dynamics. Here, we recorded electroencephalography in younger and older human adults (of both sexes) to investigate how aging affects neural synchronization to 4-Hz amplitude-modulated noises with different envelope shapes (ramped: slow attack & sharp decay; damped: sharp attack & slow decay). We observed that subcortical responses did not differ between age groups, whereas older compared to younger adults exhibited larger cortical responses to sound onsets, consistent with an increase in auditory cortical excitability. Neural activity in older adults synchronized more strongly with rapid-onset, slow-offset (damped) envelopes, was less sinusoidal, and more peaked. Younger adults demonstrated the opposite pattern, showing stronger synchronization with slow-onset, rapid-offset (ramped) envelopes, as well as a more sinusoidal neural response shape. The current results suggest that age-related changes in the excitability of auditory cortex alter responses to envelope dynamics. This may be part of the reason why older adults experience difficulty understanding speech in noise.

Significance StatementMany middle aged and older adults report difficulty understanding speech when there is background noise, which can trigger social withdrawal and negative psychosocial health outcomes. The difficulty may be related to age-related changes in how the brain processes temporal sound features. We tested younger and older people on their sensitivity to different envelope shapes, using EEG. Our results demonstrate that aging is associated with heightened sensitivity to sounds with a sharp attack and gradual decay, and sharper neural responses that deviate from the sinusoidal features of the stimulus, perhaps reflecting increased excitability in the aged auditory cortex. Altered responses to temporal sound features may be part of the reason why older adults often experience difficulty understanding speech in social situations.

category: neuroscience


title: Time scale separation of information processing between sensory and associative regions

abstract: A hierarchy of local timescales with a back (sensory)-to-front (prefrontal) gradient reflects brain region specialization. However, cognitive processes emerge from the coordinated activity across regions, and the corresponding timescales should refer to the interactions rather than to regional activity. Using edgewise connectivity on magnetoencephalography signals, we demonstrate a reverse front-to-back gradient when non-local interactions are prominent. Thus, the timescales are dynamic and reconfigure between back-to-front and front-to-back patterns.


title: Dynamical interactions reconfigure the gradient of cortical timescales


title: Attribution of Cancer Origins to Endogenous, Exogenous, and Actionable Mutational Processes

abstract: Mutational processes in tumors leave tell-tale genomic signatures composed of "passenger" mutations and mutations that have quantifiable effects on the proliferation and survival of cancer cell lineages. We identify the contributions of mutational processes to each oncogenic variant, quantifying responsibility for origination of changes at oncogenic variant sites contributing to tumorigenesis in 23 cancer types. We demonstrate that the variants driving melanomas and lung cancers are predominantly attributable to the actionable, preventable, exogenous mutational processes of ultraviolet light and tobacco exposure, whereas gliomas and prostate adenocarcinomas are largely attributable to endogenous processes associated with aging. Preventable mutations associated with pathogen exposure and APOBEC activity account for a large proportion of the cancer effect within head and neck, bladder, cervical, and breast cancers. These attributions complement epidemiological approaches--revealing the burden of cancer driven by single-nucleotide variants caused by either endogenous or exogenous, non-actionable or actionable processes, and crucially inform cancer prevention.

category: cancer biology


title: Geometric and Topological Approaches to Shape Variation in Ginkgo Leaves

abstract: Leaf shape is a key plant trait that varies enormously. The diversity of leaf shape, and the range of applications for data on this trait, requires frequent methodological developments so that researchers have an up-to-date toolkit with which to quantify leaf shape. We generated a dataset of 468 leaves produced by Ginkgo biloba, and 24 fossil leaves produced by evolutionary relatives of extant Ginkgo. We quantified the shape of each leaf by developing a geometric method based on elastic curves and a topological method based on persistent homology. Our geometric method indicates that shape variation in our modern sample is dominated by leaf size, furrow depth, and the angle of the two lobes at the base of the leaf that is also related to leaf width. Our topological method indicates that shape variation in our modern sample is dominated by leaf size and furrow depth. We have applied both methods to modern and fossil material: the methods are complementary, identifying similar primary patterns of variation, but also revealing some different aspects of morphological variation. Our topological approach distinguishes long-shoot leaves from short-shoot leaves and both methods indicate that leaf shape influences or is at least related to leaf area.

category: plant biology


title: Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

abstract: The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation - including static extrinsic noise - exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalises concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

category: systems biology


title: Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species

abstract: AO_SCPLOWBSTRACTC_SCPLOWFemale endoparasitoid wasps usually inject venom into hosts to suppress their immune response and ensure offspring development. However, the parasitoids ability to evolve towards increased success on a given host simultaneously with the evolution of the composition of its venom has never been demonstrated. Here, we designed an experimental evolution to address this question. We crossed two parasitoid lines of Leptopilina boulardi differing both in parasitic success on different Drosophila hosts and venom composition. F2 descendants were reared on three different Drosophila species for nine generations. We tested for evolution of parasitic success over the generations and for the capacity of parasitoids selected on a given host to succeed on another host. We also tested whether the venom composition - based on a statistical analysis of the variation in intensity of the venom protein bands on SDS-PAGE 1D - evolved in response to different host species. Results showed a specialization of the parasitoids on their selection host and a rapid and differential evolution of the venom composition according to the host. Overall, data suggest a high potential for parasitoids to adapt to a new host, which may have important consequences in the field as well in the context of biological control.

category: evolutionary biology


title: Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

abstract: Species harbor extensive structural variation underpinning recent adaptive evolution and major disease phenotypes. Most sequence rearrangements are generated non-randomly along the genome through non-allelic recombination and transposable element activity. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a major fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We used machine-learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Low-copy LINE and Gypsy retrotransposons explain most inversion, indel and duplication events. We retrain our model on Arabidopsis thaliana and show that our modelling approach can be extended to more complex genomes. Finally, we analyzed complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations were enriched for newly generated structural variation consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation predicted the position of >74% of the newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.

category: genomics


title: Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

abstract: The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios--the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery--is surprisingly diffcult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

category: biophysics


title: A tunable population timer in multicellular consortia

abstract: Processing time-dependent information requires cells to quantify the duration of past regulatory events and program the time span of future signals. At the single-cell level, timer mechanisms can be implemented with genetic circuits: sets of genes connected to achieve specific tasks. However, such systems are difficult to implement in single cells due to saturation in molecular components and stochasticity in the limited intracellular space. Multicellular implementations, on the other hand, outsource some of the components of information-processing circuits to the extracellular space, and thereby might escape those constraints. Here we develop a theoretical framework, based on a trilinear coordinate representation, to study the collective behavior of a cellular population composed of three cell types under stationary conditions. This framework reveals that distributing different processes (in our case the production, detection and degradation of a time-encoding signal) across distinct cell types enables the robust implementation of a multicellular timer. Our analysis also shows the circuit to be easily tunable by varying the cellular composition of the consortium.

category: synthetic biology


title: Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment

abstract: Linkage-Disequilibrium Score Regression (LDSC) is a popular framework for analyzing GWAS summary statistics that allows for estimating SNP heritability, confounding, and functional enrichment of genetic variants with different annotations. Recent work has highlighted the influence of implicit and explicit assumptions of the model on the biological interpretation of the results. In this work, we explored a formulation of LDSC that replaces the r2 measure of LD with a recently-proposed unbiased estimator of the D2 statistic. In addition to modest statistical difference across estimators, this derivation highlighted implicit and unrealistic assumptions about the relationship between allele frequency, effect size, and annotation status. We carry out a systematic comparison of alternative LDSC formulations by applying them to summary statistics from 47 GWAS traits. Our results show that commonly used models likely underestimate functional enrichment. These results highlight the importance of calibrating the LDSC model to achieve a more robust understanding of polygenic traits.

category: genetics


title: Life history tradeoffs, division of labor and evolutionary transitions in individuality

abstract: Reproductive division of labor has been proposed to play a key role for evolutionary transitions in individuality (ETIs). This chapter provides a guide to a theoretical model that addresses the role of a tradeoff between life-history traits in selecting for a reproductive division of labor during the transition from unicellular to multicellular organisms. In particular, it focuses on the five key assumptions of the model, namely (1) fitness is viability times fecundity; (2) collective traits are linear functions of their cellular counterparts; (3) there is a tradeoff between cell viability and fecundity; (4) cell contribution to the collective is optimal; and (5) there is an initial reproductive cost in large collectives. Thereafter the chapter contrasts two interpretations of the model in the context of ETIs. Originally, the model was interpreted as showing that during the transition to multicellularity the fitness of the lower-level (the cells) is "transferred" to the higher level (the collective). Despite its apparent intuitiveness, fitness transfer may obscure actual mechanisms in metaphorical language. Thus, an alternative and more conservative interpretation of the model that focuses on cell traits and the evolutionary constraints that link them is advocated. In addition, it allows for pursuing subsequent questions, such as the evolution of development.


title: A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl

abstract: 1Current methods for genotype imputation and phasing exploit the sheer volume of data in haplotype reference panels and rely on hidden Markov models. Existing programs all have essentially the same imputation accuracy, are computationally intensive, and generally require pre-phasing the typed markers. We propose a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for hidden Markov model calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage, and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs. Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing.


title: Neural networks to learn protein sequence-function relationships from deep mutational scanning data

abstract:
The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a proteins behavior and properties. We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a networks internal representation affects its ability to learn the sequence-function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks ability to learn biologically meaningful information about protein structure and mechanism. Finally, we demonstrate the models ability to navigate sequence space and design new proteins beyond the training set. We applied the GB1 models to design a sequence that binds to IgG with substantially higher affinity than wild-type GB1. Our software is available from https://github.com/gitter-lab/nn4dms.

SignificanceUnderstanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from sequence to function is tremendously complex because it involves thousands of molecular interactions that are coupled over multiple lengths and timescales. In this work, we show neural networks can learn the sequence-function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequencefunction relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate the learned models can be applied to design new proteins with properties that exceed natural sequences.

category: bioinformatics


title: Alleloscope: Integrative single cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

abstract: Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.


title: Connectome Gradient Dysfunction in Major Depression and Its Association with Gene Expression Profiles

abstract:
BackgroundPatients with major depressive disorder (MDD) exhibit concurrent deficits in sensory processing and high-order cognitive functions such as self-awareness and rumination. Connectome mapping studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this principal connectome gradient is disrupted in patients with MDD and how this disruption is associated with gene expression profiles remain unclear.

MethodsUsing a large cohort of resting-state functional magnetic resonance imaging data from 2,234 participants (1,150 patients with MDD and 1,084 healthy controls) recruited at 10 sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth and postmortem gene expression data to assess the cognitive functions and transcriptional profiles related to the gradient alterations in MDD, respectively.

ResultsRelative to controls, patients with MDD exhibited abnormal global topography of the principal primary-to-transmodal gradient, as indicated by reduced explanation ratio, gradient range, and gradient variation (Cohens d = -0.16[~]-0.21). Focal alterations of gradient scores were mostly in the primary systems involved in sensory processing and in the transmodal systems implicated in high-order cognition. The transcriptional profiles explained 53.9% of the spatial variance in the altered gradient patterns, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding.

ConclusionsThese results highlight the dysfunction of the core connectome hierarchy in MDD and its linkage with gene expression profiles, providing insights into the neurobiological and molecular genetic underpinnings of sensory-cognitive deficits in this disorder.


title: Diffusion-Informed Spatial Smoothing of fMRI Data in White Matter Using Spectral Graph Filters

abstract: Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subjects unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Projects 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.


title: Decoding subjective emotional arousal from EEG during an immersive Virtual Reality experience

abstract:
Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network.

We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.


title: Transcriptional Diversity of Medium Spiny Neurons in the Primate Striatum

abstract: The striatum is the interface between dopamine reward signals and cortico-basal ganglia circuits that mediate diverse behavioral functions. Medium spiny neurons (MSNs) constitute the vast majority of striatal neurons and are traditionally classified as direct- or indirect-pathway neurons. However, that traditional model does not explain the anatomical and functional diversity of MSNs. Here, we defined molecularly distinct MSN types in the primate striatum, including (1) dorsal striatum MSN types associated with striosome and matrix compartments, (2) ventral striatum types associated with the nucleus accumbens shell and olfactory tubercle, and (3) an MSN-like type restricted to -opioid receptor rich islands in the ventral striatum. These results lay the foundation for achieving cell type-specific transgenesis in the primate striatum and provide a blueprint for investigating circuit-specific processing.


title: Heavily burned wood from wildfires is less likely to provide functionality in streams

abstract:
O_LIIncreasingly severe forest fires are recruiting more heavily burned wood into streams. Wood affects every ecological and physical process in streams differently throughout seasons. However, little is known about the seasonality of wood functions in fire-prone biomes and how it combines with wood burning level to guide future postfire restoration efforts.
C_LIO_LIThrough an extensive three-year seasonal tracking of stream wood following forest fires in central Portugal, we examined for the first time the influence of burning level, season, and a large suite of driving factors on the likelihood of each of four functions with primary ecological consequences -- retention of organic matter, serving as substrate for aquatic biota, being key pieces forming wood jams, and deflecting flow including pool habitat formation.
C_LIO_LIOur results strongly support that one of the main ecological functions of wood in rivers, i.e. to provide substrate for biological organisms -- namely for vegetation, periphyton, biofilms, and ovipositions -- can be negatively affected in heavily burned wood.
C_LIO_LIExcept for jam formation, the probability of each stream wood function changed markedly with season and the probability of non-function was nearly twice as high in the Euro-Mediterranean dry as in the wet season.
C_LIO_LIMore anchored and decayed wood increased the probability of all functions, whereas the effect of submergence depended on the function. Challenging the "size paradigm" assuming larger-sized pieces to provide more function, our data suggest the effect of size to be function-specific.
C_LIO_LISynthesis and applications. We show how postfire restoration success can be maximized by selecting the most appropriate wood, taking advantage of attribute-function relationships and choosing the right timing for operations. We urge managers to refrain from removing wood or to selectively remove the most heavily carbonized only, allowing the persistence of great potential to provide substrate for stream biota. The non-attraction of heavily burned wood as substrate can be compensated for by other wood with attributes enhancing this function, such as wood deeper within the bankfull area, and with large diameters. These results help to inform successful management, as is increasingly asked from restoration ecology.
C_LI

category: ecology


title: CaMKII binds both substrates and activators at the active site

abstract: Ca2+/calmodulin dependent protein kinase II (CaMKII) is a signaling protein required for long-term memory. Once activated by Ca2+/CaM, it sustains activity even after the Ca2+ dissipates. In addition to well-known autophosphorylation-mediated mechanism, interaction with specific binding partners also persistently activates CaMKII. A longstanding model invokes two distinct S- and T-sites. If an interactor binds at the T-site, it will preclude autoinhibition and allow substrates to be phosphorylated at the S-site. Here, we specifically test this model with X-ray crystallography, molecular dynamics simulations, and biochemistry. Our data are inconsistent with this model. Co-crystal structures of four different activators or substrates show that they all bind to a single continuous site across the kinase domain. We propose a mechanistic model that persistent CaMKII activity is facilitated by high affinity binding partners, which kinetically compete with autoinhibition by the regulatory segment to allow substrate phosphorylation.

category: biochemistry


title: Structural basis of rotavirus RNA chaperone displacement and RNA annealing

abstract: Rotavirus genomes are distributed between eleven distinct RNA molecules, all of which must be selectively co-packaged during virus assembly. This likely occurs through sequence-specific RNA interactions facilitated by the RNA chaperone NSP2. Here, we report that NSP2 auto-regulates its chaperone activity through its C-terminal region (CTR) that promotes RNA-RNA interactions by limiting its helix-unwinding activity. Unexpectedly, structural proteomics data revealed that the CTR does not directly interact with RNA, whilst accelerating RNA release from NSP2. Cryo-electron microscopy reconstructions of an NSP2-RNA complex reveal a highly conserved acidic patch poised towards RNA. Virus replication was abrogated by charge-disrupting mutations within the acidic patch but completely restored by charge-preserving mutations. Mechanistic similarities between NSP2 and the unrelated bacterial RNA chaperone Hfq suggest that accelerating RNA dissociation whilst promoting inter-molecular RNA interactions may be a widespread strategy of RNA chaperone recycling.


title: Multimodal profiling of lung granulomas reveals cellular correlates of tuberculosis control

abstract:
Mycobacterium tuberculosis lung infection results in a complex multicellular structure, the granuloma. In some granulomas, immune activity promotes bacterial clearance; in others, bacteria persist and grow. We identified correlates of bacterial control in cynomolgus macaque lung granulomas by co-registering longitudinal PET-CT imaging, single-cell RNA-sequencing, and measures of bacterial clearance. We find that bacterial persistence occurs in granulomas enriched for mast, endothelial, fibroblast and plasma cells, signaling amongst themselves via Type II immunity and wound healing pathways. In contrast, these interactions are largely absent in granulomas that drive bacterial control, which are often those that form later in the course of infection; these restrictive lesions are characterized by cellular ecosystems enriched for Type1-Type17, stem-like, and cytotoxic T cells engaged in pro-inflammatory signaling networks that involve diverse myeloid and non-immune cell populations. There is also a temporal aspect to bacterial control, in that granulomas that arise later in infection (in the context of an established immune response) share the functional characteristics of restrictive granulomas and are more capable of killing Mtb. Taken together, our results define the complex multicellular ecosystems underlying (lack of) granuloma resolution and highlight host immune targets that can be leveraged to develop new vaccine and therapeutic strategies for TB.

One-Sentence SummaryBacterial control in TB lung granulomas correlates with distinct cellular immune microenvironments and time of formation after infection.

category: immunology


title: Deep-learning based identification, pose estimation and end-to-end behavior classification for interacting primates and mice in complex environments

abstract: The quantification of behaviors of interest from video data is commonly used to study brain function, the effects of pharmacological interventions, and genetic alterations. Existing approaches lack the capability to analyze the behavior of groups of animals in complex environments. We present a novel deep learning architecture for classifying individual and social animal behavior, even in complex environments directly from raw video frames, while requiring no intervention after initial human supervision. Our behavioral classifier is embedded in a pipeline (SIPEC) that performs segmentation, identification, pose-estimation, and classification of complex behavior, outperforming the state of the art. SIPEC successfully recognizes multiple behaviors of freely moving individual mice as well as socially interacting non-human primates in 3D, using data only from simple mono-vision cameras in home-cage setups.


title: Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments


title: Biotransformation of lindane (γ-hexachlorocyclohexane) to non-toxic end products by sequential treatment with three mixed anaerobic microbial cultures

abstract:
The {gamma} isomer of hexachlorocyclohexane (HCH), also known as lindane, is a carcinogenic persistent organic pollutant. Lindane was used worldwide as an agricultural insecticide. Legacy soil and groundwater contamination with lindane and other HCH isomers is still a big concern. The biotic reductive dechlorination of HCH to non-desirable and toxic lower chlorinated compounds such as monochlorobenzene (MCB) and benzene, among others, has been broadly documented. Here, we demonstrate for the first time that complete biotransformation of lindane to non-toxic end products is attainable using a sequential treatment approach with three mixed anaerobic microbial cultures referred to as culture I, II, and III. Biaugmentation with culture I achieved dechlorination of lindane to MCB and benzene. Culture II was able to dechlorinate MCB to benzene, and finally, culture III carried out methanogenic benzene degradation. Distinct Dehalobacter populations, corresponding to different 16S rRNA amplicon sequence variants in culture I and culture II, were responsible for lindane and MCB dechlorination, respectively. This study continues to highlight key roles of Dehalobacter spp. as chlorobenzene- and HCH-organohalide-respiring bacteria and demonstrates that sequential treatment with specialized anaerobic cultures may be explored at field sites in order to address legacy soil and groundwater contamination with HCH.

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category: microbiology


title: The Genetics and Evolution of Eye Color in Domestic Pigeons (Columba livia)

abstract: The avian eye color, generally referred to the color of the iris, results from both pigments and structural coloration. Avian iris colors exhibit striking interspecific and, in some domestic species, intraspecific variations, suggesting unique evolutionary and ecological histories. Here we tackled the genetic basis of the pearl (white) iris color in domestic pigeons (Columba livia), to elucidate the largely unknown genetic mechanism underlying the evolution of avian iris coloration. Using a genome-wide association study (GWAS) in 92 pigeons, we mapped the pearl iris trait to a 9 kb region and a facilitative glucose transporter gene SLC2A11B. A nonsense mutation W49X leading to a premature stop codon in SLC2A11B was identified as the causal variant. Transcriptome analysis suggested that SLC2A11B loss-of-function may downregulate the xanthophore-differentiation gene CSF1R, and a key gene GCH1 involved in biosynthesis of pteridine, whose absence results in pearl iris. Coalescence and phylogenetic analyses indicated the mutation originated about 5,400 years ago coinciding with the onset of pigeon domestication, while positive selection was detected likely associated with artificial breeding. Within Aves, potentially impaired SLC2A11B was found in 10 species from six distinct lineages correlated to their signature brown or blue eyes. Analysis of vertebrate SLC2A11B orthologs revealed relaxed selection in the avian clade, consistent with the scenario that, during and after avian divergence from reptile ancestor, the SLC2A11B-involved development of dermal chromatophores likely degenerated due to feather coverage. Our findings provide new insight into the mechanism of avian iris color variations and the evolution of pigmentation in vertebrates.


title: Mosaic Evolution of Cognitive Skills in Mammals

abstract: The capacity to inhibit prepotent actions (inhibitory self-control) plays an important role in many aspects of the behaviour of birds and mammals. Although a number of studies have used it as an index of foraging skills, inhibition is, in fact, also crucial for maintaining the temporal and spatial coherence of bonded social groups. Using two sets of comparative data, we show that, in primates, the capacity to inhibit behaviour correlates better with the demands of social contexts than the demands of foraging contexts, whereas a more generalised capacity for causal reasoning correlates with both social and foraging contexts. In addition, we confirm the Passingham-Wise Conjecture that the capacity to inhibit prepotent action is unique to anthropoid primates, and suggest that it may be crucial for the spatio-temporal integrity of their unique bonded social groups.

category: animal behavior and cognition


title: The Social Role of Inhibition in Primates and Other Animals


title: A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis

abstract:
BackgroundThere are few (if any) practical guidelines for predictive and falsifiable multi-omics data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules.

MethodsWe assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omics integration of 19 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor associated genes from several independent cohorts.

ResultsOur benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS-genes. The multi-omics case study using MS revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module was differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10-47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS.

ConclusionWe believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omics approaches for complex diseases.


title: A novel DNA-binding protein regulates sexual differentiation in malaria parasites and is essential for transmission.

abstract: Transmission of Plasmodium falciparum and other malaria parasites requires their differentiation from asexual blood stages into gametocytes, the non-replicative sexual stage necessary for transmission to the mosquito vector. This transition involves changes in gene expression and chromatin reorganization that result in the activation and silencing of stage-specific genes. However, the genomes of malaria parasites have been noted for their dearth of transcriptional and chromatin regulators and the molecular mediators of these changes remain largely unknown. We recently identified HomeoDomain Protein 1 (HDP1) as a DNA-binding protein that enhances the expression of key genes that are critical for early sexual differentiation. The discovery of a homeodomain-like DNA-binding protein marks a new class of transcriptional regulator in malaria parasites outside of the better-characterized ApiAP2 family. In this study, we show that HDP1 facilitates the necessary upregulation of inner membrane complex components during early gametocytogenesis that gives P. falciparum gametocytes they characteristic shape and is required for gametocyte maturation and parasite transmission.


title: The essential transcriptional regulator HDP1 drives expansion of the inner membrane complex during early sexual differentiation of malaria parasites.


title: Characterization and structural prediction of the putative ORF10 protein in SARS-CoV-2

abstract: Upstream of the 3-untranslated region in the SARS-CoV-2 genome is ORF10 which has been proposed to encode for the ORF10 protein. Current research is still unclear on whether this protein is synthesized, but further investigations are still warranted. Herein, this study uses multiple bioinformatic tools to biochemically and functionally characterize the ORF10 protein, along with predicting its tertiary structure. Results indicate a highly ordered, hydrophobic, and thermally stable protein that contains at least one transmembrane region. This protein also possesses high residue protein-binding propensity, primarily in the N-terminal half. An assessment of forty-one missense mutations reveal slight changes in residue flexibility, mainly in the C-terminal half. However, these same mutations do not inflict significant changes on protein stability and other biochemical features. The predicted model suggests the ORF10 protein contains a {beta}--{beta} motif with a {beta}-molecular recognition feature occurring in the first {beta}-strand. Functionally, the ORF10 protein could be a membrane protein. A single pocket was identified in this protein but found to possess low druggability. The ORF10 itself consists of two distinct lineages: the SARS-CoV lineage and the SARS-CoV-2 lineage. Evidence of strong positive selection (dN/dS = 4.01) and purifying selection (dN/dS = 0.713) were found within the SARS-CoV-2 lineage and SARS-CoV lineage, respectively. Collectively, these results continue to assess the biological relevance of ORF10 and its putatively encoded protein, thereby aiding in diagnostic and possibly vaccine development.


title: Ancestral circuits for vertebrate colour vision emerge at the first retinal synapse

abstract: For colour vision, retinal circuits separate information about intensity and wavelength. This requires comparison of at least two spectrally distinct photoreceptors, as in the case of most mammals. However, many vertebrates use the full complement of four  ancestral cone-types ( red,  green,  blue,  UV), and in those cases the nature and implementation of this computation remains poorly understood. Here, we establish the complete circuit architecture of outer retinal circuits underlying colour processing in larval zebrafish, which involves the full ancestral complement of four cone- and three horizontal cell types. Our findings reveal that the synaptic outputs of red- and green-cones efficiently rotate the encoding of natural daylight in a principal component analysis (PCA)-like manner to yield primary achromatic and spectrally-opponent axes, respectively. Together, these two cones capture 91.3% of the spectral variance in natural light. Next, blue-cones are tuned so as to capture most remaining variance when opposed to green-cones. Finally, UV-cones present a UV-achromatic axis for prey capture. We note that fruit flies - the only other tetrachromat species where comparable circuit-level information is available - use essentially the same strategy to extract spectral information from their relatively blue-shifted terrestrial visual world. Together, our results suggest that rotating colour space into primary achromatic and chromatic axes at the eyes first synapse may be a fundamental principle of colour vision when using more than two spectrally well-separated photoreceptor types.


title: LINE-1 retrotransposons facilitate horizontal gene transfer into poxviruses

abstract: There is ample phylogenetic evidence that many critical virus functions, like immune evasion, evolved by the acquisition of genes from their hosts through horizontal gene transfer (HGT). However, the lack of an experimental system has prevented a mechanistic understanding of this process. We developed a model to elucidate the mechanisms of HGT into vaccinia virus, the prototypic poxvirus. All identified gene capture events showed signatures of long interspersed nuclear element-1 (LINE-1)-mediated retrotransposition, including spliced-out introns, polyadenylated tails and target site duplications. In one case, the acquired gene integrated together with a polyadenylated host U2 small nuclear RNA. Integrations occurred across the genome, in some cases knocking out essential viral genes. These essential gene knockouts were rescued through a process of complementation by the parent virus followed by non-homologous recombination during serial passaging to generate a single competent virus. This work links multiple evolutionary mechanisms into one adaptive cascade and identifies host retrotransposons as major drivers for virus evolution.


title: Nitric Oxide Regulates the Ralstonia solanacearum Type 3 Secretion System

abstract: Ralstonia solancearum causes bacterial wilt disease on diverse plant hosts. R. solanacearum cells enter a host from soil or infested water through the roots, then multiply and spread in the water-transporting xylem vessels. Despite the low nutrient content of xylem sap, R. solanacearum grows very well inside the host, using denitrification to respire in this hypoxic environment. R. solanacearum growth in planta also depends on the successful deployment of protein effectors into host cells via a Type III Secretion System (T3SS). The T3SS is absolutely required for R. solanacearum virulence, but it is metabolically costly and can trigger host defenses. Thus, the pathogens success depends on optimized regulation of the T3SS. We found that a byproduct of denitrification, the toxic free-radical nitric oxide (NO), positively regulates the R. solanacearum T3SS both in vitro and in planta. Using chemical treatments and R. solanacearum mutants with altered NO levels, we show that the expression of a key T3SS regulator is induced by NO in culture. Analyzing the transcriptome of R. solanacearum responding to varying levels of NO both in culture and in planta revealed that the T3SS and effectors were broadly upregulated with increasing levels of NO. This regulation was specific to the T3SS and was not shared by other stressors. Our results suggest that R. solanacearum experiences an NO-rich environment in the plant host and may use this NO as a signal to activate T3SS during infection.


title: Principal component analysis of RNA-seq data unveils a novel prostate cancer-associated gene expression signature

abstract: Prostate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1), validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating drivers genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis comprising major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures may describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.


title: Post-exposure protection of SARS-CoV-2 lethal infected K18-hACE2 transgenic mice by neutralizing human monoclonal antibody

abstract: Coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits high levels of mortality and morbidity and has dramatic consequences on human life, sociality and global economy. Neutralizing antibodies constitute a highly promising approach for treating and preventing infection by this novel pathogen. In the present study, we characterized and further evaluated the recently identified human monoclonal MD65 antibody for its ability to provide protection against a lethal SARS-CoV-2 infection of K18-hACE2 transgenic mice. Eighty percent of the untreated mice succumbed 6-9 days post-infection while administration of the MD65 antibody as late as 3 days after exposure, rescued all infected animals. In addition, the efficiency of the treatment is supported by prevention of morbidity and ablation of the load of infective virions in the lungs of treated animals. The data unprecedentedly demonstrate, the therapeutic value of human monoclonal antibodies as a life-saving treatment of severe COVID-19 infection.


title: Evolutionary dynamics of neutral phenotypes under DNA substitution models

abstract: We study the evolution of quantitative molecular traits in the absence of selection. Using Felsensteins 1981 DNA substitution model, we predict a linear restoring force on the mean of an additive phenotype. Remarkably, the mean dynamics are independent of the effect sizes and genotype and are similar to those predicted by the widely-used OU model for stabilizing selection and the house-of-cards model for phenotype evolution. We confirm the predictions empirically using additive molecular phenotypes calculated from ancestral reconstructions of putatively unconstrained DNA sequences in primate genomes. We predict and confirm empirically that the dynamics of the variance are more complicated than those predicted by the OU model, and show that our results for the restoring force of mutation hold even for nonadditive phenotypes, such as number of transcription factor binding sites, longest encoded peptide and folding propensity of the encoded peptide. Our results have implications for efforts to infer selection based on quantitative phenotype dynamics as well as to understand long-term trends in evolution of quantitative molecular traits.


title: No impact of functional connectivity of the motor system on the resting motor threshold: A replication study.

abstract: The physiological mechanisms of corticospinal excitability and factors influencing its measurement with transcranial magnetic stimulation are still poorly understood. A recent study reported an impact of functional connectivity between the primary motor cortex and dorsal premotor cortex on the resting motor threshold of the dominant hemisphere. We aimed to replicate these findings in a larger sample of 38 healthy right-handed subjects with data from both hemispheres. Resting-state functional connectivity was assessed between the primary motor cortex and five a-priori defined motor-relevant regions on each hemisphere as well as interhemispherically between both primary motor cortices. Following the procedure by the original authors, we included age, the cortical grey matter volume and coil to cortex distance as further predictors in the analysis. We report replication models for the dominant hemisphere as well as an extension to data from both hemispheres and support the results with Bayes factors. Functional connectivity between the primary motor cortex and dorsal premotor cortex did not explain variability in the resting motor threshold and we obtained moderate evidence for the absence of this effect. In contrast, coil to cortex distance could be confirmed as an important predictor with strong evidence. These findings contradict the previously proposed effect, thus questioning the notion of the dorsal premotor cortex playing a major role in modifying corticospinal excitability.


title: HybPhaser: a workflow for the detection and phasing of hybrids in target capture datasets

abstract:
Premise of the studyHybrids contain divergent alleles that can confound phylogenetic analyses but can provide insights into reticulated evolution when identified and phased. We developed a workflow to detect hybrids in target capture datasets and phase reads into parental lineages using a similarity and phylogenetic framework.

MethodsWe used Angiosperms353 target capture data for Nepenthes including known hybrids to test the novel workflow. Reference mapping was used to assess heterozygous sites across the dataset, detect hybrid accessions and paralogous genes. Hybrid samples were phased by mapping reads to multiple references and sorting reads according to similarity. Phased accessions were included in the phylogenetic framework.

ResultsAll known Nepenthes hybrids and nine more samples had high levels of heterozygous sites, reads associated with multiple divergent clades, and were phased into accessions resembling divergent haplotypes. Phylogenetic analysis including phased accessions increased clade support and confirmed parental lineages of hybrids.

DiscussionHybPhaser provides a novel approach to detect and phase hybrids in target capture datasets, which can provide insights into reticulations by revealing origins of hybrids and reduce conflicting signal leading to more robust phylogenetic analyses.


title: TGS1 controls snRNA 3' end processing, prevents neurodegeneration and ameliorates SMN-dependent neurological phenotypes in vivo

abstract: Trimethylguanosine synthase 1 (TGS1) is a highly conserved enzyme that converts the 5 mono-methylguanosine cap of snRNAs to a trimethylguanosine cap. Here, we show that loss of TGS1 in C. elegans, D. melanogaster and D. rerio results in neurological phenotypes similar to those caused by Survival Motor Neuron (SMN) deficiency. Importantly, expression of human TGS1 ameliorates the SMN-dependent neurological phenotypes in both flies and worms, revealing that TGS1 can partly counteract the effects of SMN deficiency. TGS1 loss in HeLa cells leads to the accumulation of immature U2 and U4atac snRNAs with long 3 tails that are often uridylated. snRNAs with defective 3 terminations also accumulate in Drosophila Tgs1 mutants. Consistent with defective snRNA maturation, TGS1 and SMN mutant cells also exhibit partially overlapping transcriptome alterations that include aberrantly spliced and readthrough transcripts. Together, these results identify a neuroprotective function for TGS1 and reinforce the view that defective snRNA maturation affects neuronal viability and function.


title: Insights into long non-coding RNA regulation of anthocyanin carrot root pigmentation

abstract: Carrot (Daucus carota L.) is one of the most cultivated vegetable in the world and of great importance in the human diet. Its storage organs can accumulate large quantities of anthocyanins, metabolites that confer the purple pigmentation to carrot tissues and whose biosynthesis is well characterized. Long non-coding RNAs (lncRNAs) play critical roles in regulating gene expression of various biological processes in plants. In this study, we used a high throughput stranded RNA-seq to identify and analyze the expression profiles of lncRNAs in phloem and xylem root samples using two genotypes with a strong difference in anthocyanin production. We discovered and annotated 8484 new genes, including 2095 new protein-coding and 6373 non-coding transcripts. Moreover, we identified 639 differentially expressed lncRNAs between the phenotypically contrasted genotypes, including certain only detected in a particular tissue. We then established correlations between lncRNAs and anthocyanin biosynthesis genes in order to identify a molecular framework for the differential expression of the pathway between genotypes. A specific natural antisense transcript (NAT) linked to the DcMYB7 key anthocyanin biosynthetic transcription factor suggested how the regulation of this pathway may have evolved between genotypes.


title: Pseudomonas aeruginosa PA14 produces R-bodies, extendable protein polymers with roles in host colonization and virulence

abstract: Pseudomonas aeruginosa PA14, an opportunistic pathogen of diverse hosts, contains genes with the potential to confer production of R-bodies (i.e., a "reb cluster"). R-bodies are large, extendable protein polymers best known for their role in killing of paramecia by the bacterium Caedibacter taeniospiralis, and genes in the reb cluster have been implicated in PA14 virulence. Here, we present evidence that PA14 expresses reb cluster genes during colonization of plant and nematode hosts. We identify products of the reb cluster that are R-body-associated and that control stochastic expression of R-body structural genes. We also show that R-body production is required for full virulence in nematodes. Analyses of nematode ribosome content and immune response indicate that R-bodies act via a mechanism involving ribosome cleavage and translational inhibition. These observations provide insight into the biology of R-body production and its consequences during P. aeruginosa infection.


title: Hidden genetic variation in plasticity increases the potential to adapt to novel environments

abstract: If genetic variation in fitness emerges in novel environments, then rapid adaptation can overcome fitness declines and allow population persistence. Although plasticity is expected to underlie such genetic variance in fitness, this has not been tested empirically. We transplanted cuttings of a Sicilian daisy (Senecio, Asteraceae) within and outside its native range, and quantified genetic variation in fitness, which we connect to plasticity in leaf traits and gene expression. Compared to native environments, mean fitness in the novel environment declined by 87%, but was associated with a threefold increase in genetic variation. Fitness in the novel environment was genetically correlated with plasticity: genotypes with greater fitness showed higher plasticity in leaf form, lower plasticity in leaf physiology and more gene underexpression. Surprisingly, genotypes with greater fitness in the novel environment had the lowest fitness at the native site. Therefore, genetic variance in plasticity increases the potential for rapid adaptation to novel environments, but adaptive plasticity and stabilising selection in native (non-stressful) environments hides such genetic variation in natural populations.


title: Deep Neural Network Models of Object Recognition Exhibit Human-like Limitations When Performing Visual Search Tasks

abstract: To find an object we are looking for, we must recognize it. Prevailing models of visual search neglect recognition, focusing instead on selective attention mechanisms. These models account for performance limitations that participants exhibit when searching highly simplified stimuli often used in laboratory tasks. However, it is unclear how to apply these models to complex natural images of real-world objects. Deep neural networks (DNN) can be applied to any image, and recently have emerged as state-of-the-art models of object recognition in the primate ventral visual pathway. Using these DNN models, we ask whether object recognition explains limitations on performance across visual search tasks. First, we show that DNNs exhibit a hallmark effect seen when participants search simplified stimuli. Further experiments show this effect results from optimizing for object recognition: DNNs trained from randomly-initialized weights do not exhibit the same performance limitations. Next, we test DNN models of object recognition with natural images, using a dataset where each image has a visual search difficulty score, derived from human reaction times. We find DNN accuracy is inversely correlated with visual search difficulty score. Our findings suggest that to a large extent visual search performance is explained by object recognition.


title: BrainGENIE: The Brain Gene Expression and Network Imputation Engine

abstract: In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 - 11,569 genes (false-discovery rate-adjusted p<0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.


title: ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

abstract:
BackgroundDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.

ResultsWe developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals.

ConclusionsAltogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.


title: Spatiotemporal organization of movement-invariant and movement-specific signaling in the output layer of motor cortex

abstract: Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here, we recorded layer 5B population dynamics in the caudal forelimb area of motor cortex while mice performed a forelimb push/pull task and found that most neurons show movement-invariant responses, with a minority displaying movement specificity. Cell-type-specific imaging identified that movement-invariant responses dominated pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons displayed movement-specific signaling. The proportion of IT neurons decoding movement-type peaked prior to movement initiation, while for PT neurons this occurred during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being differentially routed through relatively small, distributed subpopulations of projection neurons.


title: Integrity of the short arm of nuclear pore Y-complex is required for mouse embryonic stem cell growth and differentiation

abstract:
Many cellular processes, ranging from cell division to differentiation, are controlled by nuclear pore complexes (NPCs). However studying contributions of individual NPC subunits to these processes in vertebrates has long been impeded by their complexity and the lack of efficient genetic tools. Here we use genome editing in mouse embryonic stem cells (mESCs) to characterize the role of NPC structural components, focusing on the short arm of the Y-complex that comprises Nup85, Seh1 and Nup43. We show that Seh1 and Nup43, although dispensable in pluripotent mESCs, are required for their normal cell growth rates, their viability upon differentiation, and for the maintenance of proper NPC density. mESCs with an N-terminally truncated Nup85 mutation (in which interaction with Seh1 is greatly impaired) feature a similar reduction of NPC density. However, their proliferation and differentiation are unaltered, indicating that it is the integrity of the Y-complex, rather than the number of NPCs, that is critical to ensure these processes.

Summary statementSeh1 and Nup43, although dispensable in pluripotent mouse embryonic stem cells, are required for normal cell growth, viability upon differentiation, and maintenance of proper NPC density.

category: cell biology


title: Single-cell RNA-seq analysis of human coronary arteries using an enhanced workflow reveals SMC transitions and candidate drug targets

abstract:
Background and AimsThe atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability or other plaque phenotypes are not yet available. We sought to investigate the human atherosclerotic cellular environment using scRNA-seq to uncover potential therapeutic approaches. We aimed to make our workflow user-friendly, reproducible, and applicable to other disease-specific scRNA-seq datasets.

MethodsHere we incorporate automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into an enhanced and reproducible scRNA-seq analysis workflow. Notably, we also developed an R Shiny based interactive web application to enable further exploration and analysis of the scRNA dataset.

ResultsWe applied this analysis workflow to a human coronary artery scRNA dataset and revealed distinct derivations of chondrocyte-like and fibroblast-like cells from smooth muscle cells (SMCs), and show the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several attractive avenues for future pharmacological repurposing in precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows other researchers to easily explore this dataset and benchmark applicable scRNA-seq analysis tools without prior coding knowledge.

ConclusionsThese results suggest novel effects of chemotherapeutics on the atherosclerotic cellular environment and provide future avenues of studies in precision medicine. This publicly available workflow will also allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. PlaqView allows for rapid visualization and analysis of atherosclerosis scRNA-seq datasets without the need of prior coding experience. Future releases of PlaqView will feature additional larger scRNA-seq and scATAC-seq atherosclerosis-related datasets, thus providing a critical resource for the field by promoting data harmonization and biological interpretation.


title: Exact and WKB-approximate distributions in a gene expression model with feedback in burst frequency, burst size, and protein stability

abstract: The expression of individual genes into functional protein molecules is a noisy dynamical process. Here we model the protein concentration as a jump-drift process which combines discrete stochastic production bursts (jumps) with continuous deterministic decay (drift). We allow the drift rate, the jump rate, and the jump size to depend on the protein level to implement feedback in protein stability, burst frequency, and burst size. We specifically focus on positive feedback in burst size, while allowing for arbitrary autoregulation in burst frequency and protein stability. Two versions of feedback in burst size are thereby considered: in the first, newly produced molecules instantly participate in feedback, even within the same burst; in the second, within-burst regulation does not occur due to the so-called infinitesimal delay. Without infinitesimal delay, the model is explicitly solvable; with its inclusion, an exact distribution to the model is unavailable, but we are able to construct a WKB approximation that applies in the asymptotic regime of small but frequent bursts. Comparing the asymptotic behaviour of the two model versions, we report that they yield the same WKB quasi-potential but a different exponential prefactor. We illustrate the difference on the case of a bimodal protein distribution sustained by a sigmoid feedback in burst size: we show that the omission of the infinitesimal delay overestimates the weight of the upper mode of the protein distribution. The analytic results are supported by kinetic Monte-Carlo simulations.


title: AKAP350 regulates LFA-1 organization during NK cytolytic response

abstract: Natural killer (NK) cell cytotoxicity requires extensive cytoskeleton remodeling and membrane receptor reorganization at the NK-target cell immune synapse (IS). The lymphocyte function-associated antigen (LFA)-1 accumulates at that structure, playing a central role in NK-IS assembly. The mechanisms underlying LFA-1 accumulation at the IS remain unclear. We found that AKAP350, a centrosome/Golgi associated protein, enabled NK-cytolytic activity by facilitating LFA-1 organization at the IS. Our results revealed the existence of an intracellular pool of LFA-1 that colocalized with the Golgi apparatus, which redistributed to the IS pole in control, but not in cells with decreased expression or localization of AKAP350 at the Golgi apparatus. Similarly to what was described in other cells, our results showed that NK cells nucleated microtubules at the Golgi apparatus in an AKAP350-dependent manner. Concomitantly, AKAP350 delocalization from the Golgi apparatus or pharmacological disruption of microtubule dynamics or Golgi integrity impaired LFA-1 localization at the IS. Altogether, our results unveil a novel mechanism of LFA-1 reorganization relevant for NK-cell activation, revealing the participation of the Golgi apparatus in NK-lytic IS maturation.


title: M1BP cooperates with CP190 to activate transcription at TAD borders and promote chromatin insulator activity

abstract: Genome organization is driven by forces affecting transcriptional state, but the relationship between transcription and genome architecture remains unclear. Here, we identified the Drosophila transcription factor Motif 1 Binding Protein (M1BP) in physical association with the gypsy chromatin insulator core complex, including the universal insulator protein CP190. M1BP is required for enhancer-blocking and barrier activities of the gypsy insulator as well as its proper nuclear localization. Genome-wide, M1BP specifically colocalizes with CP190 at Motif 1-containing promoters, which are enriched at topologically associating domain (TAD) borders. M1BP facilitates CP190 chromatin binding at many shared sites and vice versa. Both factors promote Motif 1-dependent gene expression and transcription near TAD borders genome-wide. Finally, loss of M1BP reduces chromatin accessibility and increases both inter- and intra-TAD local genome compaction. Our results reveal physical and functional interaction between CP190 and M1BP to activate transcription at TAD borders and mediate chromatin insulator-dependent genome organization.


title: Binding Mode of SARS-CoV2 Fusion Peptide to Human Cellular Membrane

abstract:
Infection of human cells by the SARS-CoV2 relies on its binding to a specific receptor and subsequent fusion of the viral and host cell membranes. The fusion peptide (FP), a short peptide segment in the spike protein, plays a central role in the initial penetration of the virus into the host cell membrane, followed by the fusion of the two membranes. Here, we use an array of molecular dynamics (MD) simulations taking advantage of the Highly Mobile Membrane Mimetic (HMMM) model, to investigate the interaction of the SARS-CoV2 FP with a lipid bilayer representing mammalian cellular membranes at an atomic level, and to characterize the membrane-bound form of the peptide. Six independent systems were generated by changing the initial positioning and orientation of the FP with respect to the membrane, and each system was simulated in five independent replicas, each for 300 ns. In 73% of the simulations, the FP reaches a stable, membrane-bound configuration where the peptide deeply penetrated into the membrane. Clustering of the results reveals three major membrane binding modes (binding modes 1-3) where binding mode 1 populates over half of the data points. Taking into account the sequence conservation among the viral FPs and the results of mutagenesis studies establishing the role of specific residues in the helical portion of the FP in membrane association, the significant depth of penetration of the whole peptide, and the dense population of the respective cluster, we propose that the most deeply inserted membrane-bound form (binding mode 1) represents more closely the biologically relevant form. Analysis of FP-lipid interactions shows the involvement of specific residues, previously described as the "fusion active core residues", in membrane binding. Taken together, the results shed light on a key step involved in SARS-CoV2 infection with potential implications in designing novel inhibitors.

SignificanceA key step in cellular infection by the SARS-CoV2 virus is its attachment to and penetration into the plasma membrane of human cells. These processes hinge upon the membrane interaction of the viral fusion peptide, a segment exposed by the spike protein upon its conformational changes after encountering the host cell. In this study, using molecular dynamics simulations, we describe how the fusion peptide from the SARS-CoV2 virus binds human cellular membranes and characterize, at an atomic level, lipid-protein interactions important for the stability of its membrane-bound state.


title: Physical properties of the cytoplasm modulate the rates of microtubule polymerization and depolymerization

abstract: The cytoplasm is a crowded, visco-elastic environment whose physical properties change according to physiological or developmental states. How the physical properties of the cytoplasm impact cellular functions in vivo remain poorly understood. Here, we probed the effects of cytoplasmic concentration on microtubules by applying osmotic shifts to fission yeast, moss, and mammalian cells. We show that both the rates of microtubule polymerization and depolymerization scale linearly and inversely with cytoplasmic concentration; an increase in cytoplasmic concentration decreases the rates of microtubule polymerization and depolymerization proportionally, while a decrease in cytoplasmic concentration leads to the opposite. Numerous lines of evidence indicate that these effects are due to changes in cytoplasmic viscosity rather than cellular stress responses or macromolecular crowding per se. We reconstituted these effects on microtubules in vitro by tuning viscosity. Our findings indicate that, even in normal conditions, the viscosity of cytoplasm modulates the reactions underlying microtubule dynamic behaviors.


title: Learning and motor-impulse control in crows and domestic chickens

abstract: Cognitive abilities allow animals to navigate through complex, fluctuating environments. In the present study, we tested the performance of a captive group of eight crows, Corvus corone, and ten domestic chickens, Gallus gallus domesticus, in the cylinder task, as a test of motor inhibitory control, and reversal learning as a measure of learning ability and behavioural flexibility. Four crows and nine chickens completed the cylinder task, eight crows and six chickens completed the reversal learning experiment. Crows performed better in the cylinder task compared to chickens. In the reversal learning experiment, species did not significantly differ in the number of trials until learning criterion was reached. Performance in the reversal learning experiment did not correlate with performance in the cylinder task in chickens. Our results suggest crows to possess better motor inhibitory control compared to chickens. In contrast, learning performance in a reversal learning task did not differ between the species, indicating similar levels of behavioural flexibility. Interestingly, we describe notable individual differences in performance. We stress the importance not only to compare cognitive performance between species but also between individuals of the same species when investigating the evolution of cognitive skills.


title: Learning and motor inhibitory control in crows and domestic chickens


title: ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Interactions

abstract:
A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of SL. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits.

CCS CONCEPTS* Applied computing [->] Computational genomics; Health informatics; * Mathematics of computing [->] Hypothesis testing and confidence interval computation.

ACM Reference FormatHerty Liany, Anand Jeyasekharan, and Vaibhav Rajan. 2021. ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Genetic Interactions. In Proceedings of ACM Conference. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn


title: SplineDist: Automated Cell Segmentation with Spline Curves

abstract: We present SplineDist, an instance segmentation convolutional neural network for bioimages extending the popular StarDist method. While StarDist describes objects as star-convex polygons, SplineDist uses a more flexible and general representation by modelling objects as planar parametric spline curves. Based on a new loss formulation that exploits the properties of spline constructions, we can incorporate our new object model in StarDists architecture with minimal changes. We demonstrate in synthetic and real images that SplineDist produces segmentation outlines of equal quality than StarDist with smaller network size and accurately captures non-star-convex objects that cannot be segmented with StarDist.


title: Exploring the role of life history traits and introduction effort in understanding invasion success in mammals: A case study of Barbary ground squirrels

abstract: Invasive species -species that have successfully overcome the barriers of transport, introduction, establishment, and spread- are a risk to biodiversity and ecosystem function. Introduction effort is one of the main factors underlying invasion success, but life history traits are also important as they influence population growth. In this contribution, we first investigated life history traits of the Barbary ground squirrel, Atlantoxerus getulus, a species with a very low introduction effort. We then studied if their invasion success was due to a very fast life history profile by comparing their life history traits to those of other successful invasive mammals. Next, we examined whether the number of founders and/or a fast life history influences the invasion success of squirrels. Barbary ground squirrels were on the fast end of the "fast-slow continuum", but their life history was not the only contributing factor to their invasion success, as the life history profile is comparable to other invasive species that do not have such a low introduction effort. We also found that neither life history traits nor the number of founders explained the invasion success of introduced squirrels in general. These results contradict the concept that introduction effort is the main factor explaining invasion success, especially in squirrels. Instead, we argue that invasion success can be influenced by multiple aspects of the new habitat or the biology of the introduced species.


title: simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results

abstract: Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut can efficiently cluster functional terms into groups where terms showed more consistent similarities within groups and were more mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that the semantic similarity worked well with binary cut while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment which additionally provides functionalities for visualizing, summarizing and comparing the clusterings.


title: Counterfactual Hypothesis Testing of Tumor Microenvironment Scenarios Through Semantic Image Synthesis

abstract: AO_SCPLOWBSTRACTC_SCPLOWRecent multiplexed protein imaging technologies characterize cells, their spatial organization, and interactions within microenvironments at an unprecedented resolution. Although observational data can reveal spatial associations, it does not allow users to infer salient biological relationships and cellular interactions. To address this challenge, we develop a generative model that allows users to test hypotheses about the effect of cell-cell interactions on protein expression through in silico perturbation. Our Cell-Cell Interaction GAN (CCIGAN) model employs a generative adversarial network (GAN) architecture to generate high fidelity synthetic multiplexed images from semantic cell segmentations. Our approach is unique in that it learns relationships between all imaging channels simultaneously and yields biological insights from multiple imaging technologies in silico, capturing known tumor-immune cell interactions missed by other state-of-the-art GAN models.


title: Genetic diversity and population structure of razor clam Sinonovacula constricta in Ariake Bay, Japan, revealed using RAD-Seq SNP markers.

abstract: The razor clam Sinonovacula constricta is a commercially important bivalve in Japan. The current distribution of this species in Japan is limited to Ariake Bay, where the fishery stock is declining. It is necessary to understand the genetic population structure of this species in order to restore the fishery stock while preserving the genetic diversity of the clam. Here, we report for the first time the genetic population structure of S. constricta in Ariake Bay, Japan. Paired-end restriction site-associated DNA sequencing (RAD-Seq) analyzed samples of S. constricta collected from seven mudflats located along Ariake Bay. Two different genetic populations exist in Ariake Bay, one inhabiting wild habitats and the other inhabiting the transplanted area of artificial seedlings. Our results suggest that genetic differentiation occurred between these two populations (Fst value = 0.052), and a high level of genetic differentiation is maintained between the two groups. In the future, monitoring the interbreeding status of the two genetically distinct populations and the genetic differentiation within each population is important for conserving the genetic diversity of S. constricta in Japan.

category: zoology


title: High-throughput fluorescent assay for inhibitor screening of proteases from RNA viruses

abstract: Spanish flu and other influenza outbreaks, the recent Zika epidemics, and the ongoing COVID-19 pandemic are the most profound examples of severe widespread diseases that are caused by RNA viruses. Perhaps less well known yet dangerous RNA viruses cause deadly diseases such as polio, Ebola, measles, rubella, yellow fever, dengue fever and many others. To combat a particular viral disease by diminishing its spread and number of fatal cases, effective vaccines and antivirals are indispensable. Therefore, quick access to the means of discovery of new treatments for any epidemic outbreak is of great interest and in vitro biochemical assays are the basis of drug discovery. The recent outbreak of the coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demands an affordable and reliable assay for testing antivirals. Here, we developed a quick and inexpensive high-throughput fluorescent assay to test inhibitors of viral proteases. Accordingly, we employed this assay to sample inhibitors for papain-like protease from SARS-CoV-2. In addition, we validated this assay for screening inhibitors of flaviviral protease from the tick-borne encephalitis virus to emphasize a broad range of applications of our approach. This fluorescent high-throughput assay is based on fluorescent energy transfer (FRET) between two distinct fluorescent proteins (eGFP and mCherry) connected via a substrate polypeptide. When the substrate is cleaved, FRET is abolished and the change in fluorescence corresponds to reaction progress. Our data show that this assay can be used for testing the inhibitors in the 96 or 384 well plates format with robust and reproducible outcomes.


title: Species level resolution of female bladder microbiota from 16S rRNA amplicon sequencing

abstract:
The human bladder contains bacteria in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing. However, the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA amplicon sequencing is commonly used to identify bacteria in urinary specimens, but is typically restricted to genus-level identification. Our primary aim was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA amplicon sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a reference database, a 16S rRNA gene variable region and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible, and that the reference database chosen is the most important component, followed by the 16S variable region sequenced.

ImportanceSpecies-level information may deepen our understanding of associations between bladder microbiota and bladder conditions, such as lower urinary tract symptoms and urinary tract infections. The capability to identify bacterial species depends on large databases of sequences, algorithms that leverage statistics and available computer hardware, and knowledge of bacterial genetics and classification. Taken together, this is a daunting body of knowledge to become familiar with before the simple question of bacterial identity can be answered. Our results show the choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show this improvement can be achieved through the more careful application of existing methods and use of existing resources.