title: Principal component analysis reveals multiple consistent responses to naturalistic stimuli in children and adults


title: Evolution is exponentially more powerful with frequency-dependent selection

abstract:
Valiant [1] proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotypes fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment or static fitness landscape. Valiants model distinguished families of environments that are "adaptable-to" from those that are not. But this model of evolution omits the vital ecological interactions between different evolving agents - it neglects the rich biotic environment that is central to the struggle for existence.

In this article, I extend algorithmic Darwinism to include the ecological dynamics of frequency-dependent selection as a population-dependent bias to the distribution of challenges that specify an environment. Thus, extended algorithmic Darwinism suggests extended statistical queries rather than just statistical queries as the appropriate model for eco-evo dynamics. This extended algorithmic Darwinism replaces simple invasion of wild-type by a mutant-type of higher scalar fitness with an evolutionary game between wild-type and mutant-type based on their frequency-dependent fitness function. To analyze this model, I develop a game landscape view of evolution, as a generalization of the classic fitness landscape approach.

I show that this model of eco-evo dynamics on game landscapes can provide an exponential speed-up over the purely evolutionary dynamics of the strict algorithmic Darwinism. In particular, I prove that the Parity environment - which is known to be not adaptable-to under strict algorithmic Darwinism - is adaptable-to by eco-evo dynamics. Thus, the ecology of frequency-dependent selection does not just increase the tempo of evolution, but fundamentally transforms its mode. This happens even if frequency-dependence is restricted to short-time scales - such short bursts of frequency-dependent selection can have a transformative effect on the ability of populations to adapt to their environments in the long-term.

Unlike typical learning algorithms, the eco-evo dynamic for adapting to the Parity environment does not rely on Gaussian elimination. Instead, the dynamics proceed by simple isotropic mutations and selection in finite populations of just two types (the resident wild-type and invading mutant). The resultant process has two stages: (1) a quick stage of point-mutations that moves the population to one of exponentially many local fitness peaks; followed by (2) a slower stage where each  step follows a double-mutation by a point-mutation. This second stage allows the population to hop between local fitness peaks to reach the unique global fitness peak in polynomial time. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, analysis of algorithms, evolutionary games, and long-term evolution.

category: evolutionary biology


title: Widespread misregulation of inter-species hybrid transcriptomes due to sex-specific and sex-chromosome regulatory evolution

abstract:
When gene regulatory networks diverge between species, their dysfunctional expression in inter-species hybrid individuals can create genetic incompatibilities that generate the developmental defects responsible for intrinsic post-zygotic reproductive isolation. Both cis- and trans-acting regulatory divergence can be hastened by directional selection through adaptation, sexual selection, and inter-sexual conflict, in addition to cryptic evolution under stabilizing selection. Dysfunctional sex-biased gene expression, in particular, may provide an important source of sexually-dimorphic genetic incompatibilities. Here, we characterize and compare male and female/hermaphrodite transcriptome profiles for sibling nematode species Caenorhabditis briggsae and C. nigoni, along with allele-specific expression in their F1 hybrids, to deconvolve features of expression divergence and regulatory dysfunction. Despite evidence of widespread stabilizing selection on gene expression, misexpression of sex-biased genes pervades F1 hybrids of both sexes. This finding implicates greater fragility of male genetic networks to produce dysfunctional organismal phenotypes. Spermatogenesis genes are especially prone to high divergence in both expression and coding sequences, consistent with a "faster male" model for Haldanes rule and elevated sterility of hybrid males. Moreover, underdominant expression pervades male-biased genes compared to female-biased and sex-neutral genes and an excess of cis-trans compensatory regulatory divergence for X-linked genes underscores a "large-X effect" for hybrid male expression dysfunction. Extensive regulatory divergence in sex determination pathway genes likely contributes to demasculinization of XX hybrids. The evolution of genetic incompatibilities due to regulatory versus coding sequence divergence, however, are expected to arise in an uncorrelated fashion. This study identifies important differences between the sexes in how regulatory networks diverge to contribute to sex-biases in how genetic incompatibilities manifest during the speciation process.

Authors summaryAs species diverge, many mutations that affect traits do so by altering gene expression. Such gene regulatory changes also accumulate in the control of static traits, due to compensatory effects of mutation on multiple regulatory elements. Theory predicts many of these changes to cause inter-species hybrids to experience dysfunctional gene expression that leads to reduced fitness, disproportionately affecting genes biased toward expression in one sex and that localize to sex chromosomes. Our analyses of genome-wide gene expression from Caenorhabditis nematode roundworms support these predictions. We find widespread rewiring of gene regulation, despite the extensive morphological stasis and conserved expression profiles that are hallmarks of these animals. Misregulation of expression in inter-species hybrids of both sexes is most severe for genes linked to the X-chromosome, but male organismal phenotypes are most disrupted in hybrids. This fragility of male genetic networks and sex differences in regulatory evolution of local versus distant elements may underlie feminized and sterile phenotypes among hybrids. Our work clarifies how distinct components of regulatory networks evolve and contribute to sex differences in the manifestation of genetic incompatibilities in the speciation process.

category: genomics


title: Mechanistic insights into RNA binding and RNA-regulated RIG-I ubiquitination by TRIM25

abstract: TRIM25 is a ubiquitin E3 ligase active in innate immunity and cell fate decisions. Mounting evidence suggests that TRIM25's E3 ligase activity is regulated by RNAs. However, while mutations affecting RNA binding have been described, neither the precise RNA binding site has been identified nor which domains are involved. Here, we present biophysical evidence for the presence of RNA binding sites on both TRIM25 PRY/SPRY and coiled-coil domains, and map the binding site on the PRY/SPRY with residue resolution. Cooperative RNA-binding of both domains enhances their otherwise transient interaction in solution and increases the E3 ligase activity of TRIM25. We also show that TRIM25 not only binds RNA in mammalian cells but that interfering with RNA binding has an effect on cellular RIG-I ubiquitination.

category: molecular biology


title: A functional genetic toolbox for human tissue-derived organoids

abstract: Human organoid systems recapitulate key features of organs offering platforms for modelling developmental biology and disease. Tissue-derived organoids have been widely used to study the impact of extrinsic niche factors on stem cells. However, they are rarely used to study endogenous gene function due to the lack of efficient gene manipulation tools. We have systematically developed and optimised a complete genetic toolbox for tissue-derived organoids. This includes "Organoid Easytag", our efficient workflow for targeting all types of gene loci through CRISPR-mediated homologous recombination followed by flow cytometry for enriching correctly-targeted cells. Our toolbox also incorporates conditional gene knock-down, or overexpression, using tightly-inducible CRISPR interference and CRISPR activation; the first efficient application of these techniques to tissue-derived organoids. These tools will facilitate gene perturbation studies in tissue-derived organoids providing a functional counter-part to many on-going descriptive studies, such as the Human Cell Atlas Project.

category: developmental biology


title: Robust Inference of Kinase Activity using Functional Networks

abstract: Mass spectrometry enables high-throughput screening of phospho-proteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimers disease and Parkinsons disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

category: systems biology


title: A convolutional neural network for estimating synaptic connectivity from spike trains

abstract: The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains (CoNNECT). After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

category: neuroscience


title: Dopamine firing plays a double role in coding reward prediction errors and signaling motivation in a working memory task.

abstract: Little is known about how dopamine (DA) neuron firing rates behave in cognitively demanding decision-making tasks. Here we investigated midbrain DA activity in monkeys performing a discrimination task in which the animal had to use working memory (WM) to report which of two sequentially applied vibrotactile stimuli had the higher frequency. We found that perception was altered by an internal bias, likely generated by deterioration of the representation of the first frequency during the WM period. This bias greatly controlled the DA phasic response during the two stimulation periods, confirming that DA reward prediction errors reflected subjective stimulus perception. Contrastingly, tonic dopamine activity during WM was not affected by the bias and did not encode the stored frequency. More interestingly, both WM activity and phasic responses before the second stimulus negatively correlated with reaction times of the animal after the trial start cue and thus represented motivated behavior on a trial-by-trial basis. During WM, this motivation signal underwent a ramp-like increase. At the same time, motivation reduced noise in perception and, by decreasing the effect of the bias, improved performance, especially in difficult trials. Overall, our results show that DA activity was simultaneously involved in reward prediction, motivation and WM. Also, the ramping activity during the WM period suggests a possible DA role in stabilizing sustained cortical activity, hypothetically by increasing the gain communicated to prefrontal neurons in a motivation-dependent way.


title: Independent and Interacting Value Systems for Reward and Information in the Human Brain

abstract:
Theories of Prefrontal Cortex (PFC) as optimizing reward value have been widely deployed to explain its activity in a diverse range of contexts, with substantial empirical support in neuroeconomics and decision neuroscience. Theoretical frameworks of brain function, however, suggest the existence of a second, independent value system for optimizing information during decision-making. To date, however, there has been little direct empirical evidence in favor of such frameworks. Here, by using computational modeling, model-based fMRI analysis, and a novel experimental paradigm, we aim at establishing whether independent value systems exist in human PFC. We identify two regions in the human PFC which independently encode distinct value signals. These value signals are then combined in subcortical regions in order to implement choices. Our results provide empirical evidence for PFC as an optimizer of independent value signals during decision-making under realistic scenarios, with potential implications for the interpretation of PFC activity in both healthy and clinical population.

One Sentence SummaryReward and information value are independently optimized in the Human PFC and are then combined in subcortical regions in order to implement choices.


title: Novel strategies of Raman imaging for monitoring intracellular retinoid metabolism in cancer cells

abstract: We developed a label-free Raman method for whole-cell biochemical imaging to detect molecular processes that occur in normal and cancer brain cells due to retinol transport in human cancers at the level of isolated organelles. Our approach allows to create biochemical maps of retinoids localization in lipid droplets, mitochondria and nuclei in single cells. The maps were capable of discriminating triglycerides (TAG) from retinoids (RE) in lipid droplets (LD), and mitochondria providing an excellent tool to monitor intracellular retinoid metabolism. We detected spectral changes that arose in proteins and lipids due to retinoid metabolism in human cell lines of normal astrocytes and high-grade cancer cells of glioblastoma as well as in human medulloblastoma and glioblastoma tissue. Raman imaging is an effective tool for monitoring retinoids and retinol binding proteins involved in carcinogenesis by detecting unique spectral signatures of vibrations. We found two functionally distinct lipid droplets: TAG-LD, for energy storage, and RE-LD, for regulating mechanisms of signal transduction. Raman polarization measurements revealed the occurrence of conformational changes affecting discrete regions of proteins associated with retinol binding. Aberrant expression of retinoids and retinol binding proteins in human tumours were localized in lipid droplets, and mitochondria.

category: cancer biology


title: Epigenetic modulation reveals differentiation state specificity of oncogene addiction

abstract: Hyperactivation of the MAPK signaling pathway motivates the clinical use of MAPK inhibitors for BRAF-mutant melanomas. Heterogeneity in differentiation state due to epigenetic plasticity, however, results in cell-to-cell variability in the state of MAPK dependency, diminishing the efficacy of MAPK inhibitors. To identify key regulators of such variability, we screened 276 epigenetic-modifying compounds, individually or combined with MAPK inhibitors, across genetically diverse and isogenic populations of melanoma cells. Following single-cell analysis and multivariate modeling, we identified three classes of epigenetic inhibitors that target distinct epigenetic states associated with either one of the lysine-specific histone demethylases KDM1A or KDM4B, or BET bromodomain proteins. While melanocytes remained insensitive, the anti-tumor efficacy of each inhibitor was predicted based on melanoma cells differentiation state and MAPK activity. Our systems pharmacology approach highlights a path toward identifying actionable epigenetic factors that extend the BRAF oncogene addiction paradigm on the basis of tumor cell differentiation state.


title: Data-driven learning how oncogenic gene expression locally alters heterocellular networks

abstract: Discovering and developing pharmaceutical drugs increasingly relies on mechanistic mathematical modeling and simulation. In immuno-oncology, models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity provide an important complement to wet experiments, given the cellular complexity and dynamics within tumors. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation by experts, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms have been developed to limit a priori biases in formulating mechanistic models. To realize an equivalent approach for cell-level networks, we combined digital cytometry with Bayesian network inference to generate causal models that link an increase in gene expression associated with onco-genesis with alterations in stromal and immune cell subsets directly from bulk transcriptomic datasets. To illustrate, we predicted how an increase in expression of Cell Communication Network factor 4 (CCN4/WISP1) altered the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Digital cytometry and network inference predictions were then tested using two immunocompetent mouse models for melanoma, which provided consistent experimental results.


title: Nothing better to do? Environment quality and the evolution of cooperation by partner choice

abstract: The effects of partner choice have been documented in a large number of biological systems such as sexual markets, interspecific mutualisms, or human cooperation. There are, however, a number of situations in which one would expect this mechanism to play a role, but where no such effect has ever been demonstrated. This is the case in particular in many intraspecific interactions, such as collective hunts, in non-human animals. Here we use individual-based simulations to solve this apparent paradox. We show that the conditions for partner choice to operate are in fact restrictive. They entail that individuals can compare social opportunities and choose the best. The challenge is that social opportunities are often rare because they necessitate the co-occurrence of (i) at least one available partner, and (ii) a resource to exploit together with this partner. This has three consequences. First, partner choice cannot lead to the evolution of cooperation when resources are scarce, which explains that this mechanism could never be observed in many cases of intraspecific cooperation in animals. Second, partner choice can operate when partners constitute in themselves a resource, which is the case in sexual interactions and interspecific mutualisms. Third, partner choice can lead to the evolution of cooperation when individuals live in a rich environment, and/or when they are highly efficient at extracting resources from their environment.


title: Heritable functional architecture in human visual cortex

abstract:
How much of the functional organization of our visual system is inherited? Here we tested the heritability of retinotopic maps in human visual cortex using functional magnetic resonance imaging. We demonstrate that retinotopic organization shows a closer correspondence in monozygotic (MZ) compared to dizygotic (DZ) twin pairs, suggesting a partial genetic determination. Using population receptive field (pRF) analysis to examine the preferred spatial location and selectivity of these neuronal populations, we estimate a heritability around 30% for polar angle preferences and spatial selectivity, as quantified by pRF size, in extrastriate areas V2 and V3. Our findings are consistent with heritability in both the macroscopic arrangement of visual regions and stimulus tuning properties of visual cortex. This could constitute a neural substrate for variations in a range of perceptual effects, which themselves have been found to be at least partially genetically determined. These findings also add convergent evidence for the hypothesis that functional map topology is linked with cortical morphology.

HighlightsO_LIWe analyzed retinotopic maps from monozygotic and dizygotic twin pairs
C_LIO_LIVisual field maps in V1-V3 are more similar in monozygotic twins
C_LIO_LIHeritability is greater in V1 and V3 for polar angle and population receptive field sizes
C_LIO_LIEccentricity maps show lesser degree of heritability
C_LIO_LIFurther evidence for link between cortical morphology and topology of retinotopic maps
C_LI

Graphical Abstract

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title: The paraventricular thalamus provides a polysynaptic brake on limbic CRF neurons to sex-dependently blunt binge alcohol drinking and avoidance behavior

abstract: Bed nucleus of the stria terminalis (BNST) neurons that synthesize and release the stress neuropeptide corticotropin-releasing factor (CRF) drive binge alcohol drinking and anxiety, behaviors that are primary risk factors for alcohol use disorder (AUD) and comorbid neuropsychiatric diseases more common in women than men. Here, we show that female C57BL/6J mice binge drink more than males and have greater basal BNSTCRF neuron excitability and synaptic excitation. We identified a dense VGLUT2+ glutamatergic synaptic input from the paraventricular thalamus (PVT) that is anatomically similar in males and females. These PVTBNST neurons release glutamate directly onto BNSTCRF neurons but also engage a large BNST interneuron population to ultimately provide a net inhibition of BNSTCRF neurons, and both components of this polysynaptic PVTVGLUT2-BNSTCRF circuit are more robust in females than males. Chemogenetic inhibition of the PVTBNST projection promoted binge alcohol drinking in females without affecting males, and chemogenetic activation of the pathway was sufficient to reduce avoidance behavior in both sexes in anxiogenic contexts. Lastly, we show that withdrawal from repeated binge drinking produces a female-like phenotype in the male PVT-BNSTCRF excitatory synapse without altering the function of PVTBNST neurons per se. Our data describe a complex feedforward inhibitory PVTVGLUT2-BNSTCRF glutamatergic circuit that is more robust in females, plays sex-dependent roles in alcohol drinking and avoidance behavior, and undergoes sex-dependent alcohol-induced plasticity.


title: Subfunctionalization of paralog transcription factors contributes to regulation of alkaloid pathway branch choice in Catharanthus roseus

abstract:
Catharanthus roseus produces a diverse range of specialized metabolites of the monoterpenoid indole alkaloid (MIA) class in a heavily branched pathway. Recent great progress in identification of MIA biosynthesis genes revealed that the different pathway branch genes are expressed in a highly cell type- and organ-specific and stress-dependent manner. This implies a complex control by specific transcription factors (TFs), only partly revealed today. We generated and mined a comprehensive compendium of publicly available C. roseus transcriptome data for MIA pathway branch-specific TFs. Functional analysis was performed through extensive comparative gene expression analysis and profiling of over 40 MIA metabolites in the C. roseus flower petal expression system. We identified additional members of the known BIS and ORCA regulators. Further detailed study of the ORCA TFs suggests subfunctionalization of ORCA paralogs in terms of target gene-specific regulation and synergistic activity with the central jasmonate response regulator MYC2. Moreover, we identified specific amino acid residues within the ORCA DNA-binding domains that contribute to the differential regulation of some MIA pathway branches. Our results advance our understanding of TF paralog specificity for which, despite the common occurrence of closely related paralogs in many species, comparative studies are scarce.

SIGNIFICANCE STATEMENTA gene discovery program for regulators of monoterpenoid indole alkaloid biosynthesis in Catharanthus roseus advances our understanding of paralog specificity and subfunctionalization of the renowned class of ORCA transcription factors, particularly in terms of target gene-specificity and synergistic activity with other jasmonate-responsive transcription factors.

category: plant biology


title: Delta-range coupling between prefrontal cortex and hippocampus supported by respiratory rhythmic input from the olfactory bulb

abstract: An explosion of recent findings firmly demonstrated that brain activity and cognitive function in rodents and humans are modulated synchronously with nasal respiration. Rhythmic respiratory (RR) coupling of wide-spread forebrain activity was confirmed using advanced techniques, including current source density analysis, single unit firing, and phase modulation of local gamma activity, creating solid premise for investigating how higher networks use this mechanism in their communication. Here we show essential differences in the way prefrontal cortex (PFC) and hippocampus (HC) process the RR signal from the olfactory bulb (OB) allowing dynamic PFC-HC coupling utilizing this input. We used inter-regional coherences and their correlations in rats, breathing at low rate ([~]2 Hz) at rest, outside of the short sniffing bouts. We found strong and stable OB-PFC coherence, contrasting OB-HC coherence which was low but highly variable. PFC-HC coupling, however, primarily correlated with the latter, indicating that HC access to the PFC output is dynamically regulated by the responsiveness of HC to the common rhythmic drive. This pattern was present in both theta and non-theta states of waking, whereas PFC-HC communication appeared protected from RR synchronization in sleep states. The findings help to understand the mechanism of rhythmic modulation of non-olfactory cognitive processes by the on-going regular respiration, reported in rodents as well as humans. These mechanisms may be impaired when nasal breathing is limited or in OB-pathology, including malfunctions of the OB epithelium due to infections, such as in COVID-19.


title: Delta-range coupling between prefrontal cortex and hippocampus supported by respiratory rhythmic input from the olfactory bulb in freely behaving rats


title: Convergent organization of aberrant MYB complex controls oncogenic gene expression in acute myeloid leukemia

abstract: Dysregulated gene expression contributes to most prevalent features in human cancers. Here, we show that most subtypes of acute myeloid leukemia (AML) depend on the aberrant assembly of MYB transcriptional co-activator complex. By rapid and selective peptidomimetic interference with the binding of CBP/P300 to MYB, but not CREB or MLL1, we find that the leukemic functions of MYB are mediated by CBP/P300 co-activation of a distinct set of transcription factor complexes. These MYB complexes assemble aberrantly with LYL1, E2A, C/EBP family members, LMO2 and SATB1. They are organized convergently in genetically diverse subtypes of AML, and are at least in part associated with inappropriate transcription factor co-expression. Peptidomimetic remodeling of oncogenic MYB complexes is accompanied by specific proteolysis and dynamic redistribution of CBP/P300 with alternative transcription factors such as RUNX1 to induce myeloid differentiation and apoptosis. Thus, aberrant assembly and sequestration of MYB:CBP/P300 complexes provide a unifying mechanism of oncogenic gene expression in AML. This work establishes a compelling strategy for their pharmacologic reprogramming and therapeutic targeting for diverse leukemias and possibly other human cancers caused by dysregulated gene control.


title: A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach

abstract:
Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model.

Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements.

We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

category: bioinformatics


title: Myosin-X and talin modulate integrin activity at filopodia tips

abstract: Filopodia assemble unique integrin-adhesion complexes to sense the extracellular matrix. However, the mechanisms of integrin localization and regulation in filopodia are poorly defined. Here, we observed that active integrins accumulate at the tip of myosin-X(MYO10)-positive filopodia while inactive integrins are uniformly distributed. RNAi depletion of 10 integrin activity modulators identified talin as the principal integrin activator in filopodia. Deletion of the MYO10-FERM domain, or mutation of the {beta}1-integrin-binding residues within, revealed MYO10 as facilitating integrin activation but not transport in filopodia. However, MYO10-FERM alone could not activate integrins, potentially due to dual binding to both a- and {beta}-integrin tails. As swapping MYO10-FERM with talin-FERM enabled integrin activation in filopodia, our data indicate that an integrin-binding FERM domain coupled to a myosin motor is a core requirement for integrin activation in filopodia. Therefore, we propose a two-step integrin activation model in filopodia: receptor tethering by MYO10 followed by talin-mediated integrin activation.

category: cell biology


title: A comprehensive portrait of antimicrobial resistance in the zoonotic pathogen Streptococcus suis

abstract: Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. Pigs receive more antimicrobials than most other livestock, and are a known source of zoonotic disease. We studied AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into "resistant" and "susceptible", highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multi-drug resistant isolates. And similar levels of resistance were found in pigs and humans from zoonotic regions. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many variants of small effect, appearing in a characteristic order. Our results confirm the potential for genomic data to aid in the fight against AMR, but also demonstrate that it cannot be tackled one species or one drug at a time.

category: microbiology


title: Delay of gratification in non-human animals: A review of inter-specific variation in performance

abstract: The ability to regulate and withhold an immediate behaviour in pursuit of a more preferred or valuable, albeit delayed, outcome is regarded as an important cognitive ability enabling adaptive decision-making in both social and asocial contexts. Abilities to cope with a delay in gratification have been investigated in a range of species using a variety of experimental paradigms. The present study attempts a first systematic evaluation of available experimental data from non-human animals, which is an essential basis for quantifying biological and non-biological factors (e.g. socio-ecology versus experimental design) affecting performance in delay of gratification tasks. Data were sourced from 52 separate studies, and a comprehensive overview of the available literature on delay of gratification in non-human animals is presented. We present data from 21 species, spanning across eight taxonomic order, with 1-9 species tested per taxonomic group. We highlight variation in experimental paradigms used to study delay of gratification abilities in non-human animals, both with regard to reward type or experimental setup, and discuss the implications for comparative analyses. We conclude that, at present, cross-species comparisons of delay of gratification abilities are hindered by a lack of consistency in experimental designs and low number of species tested across taxonomic orders. We hope to stimulate research on a more diverse set of species, and that future studies consider potential social and ecological factors that cause intra-specific variation in test performances, that is repeatedly seen across species.

category: animal behavior and cognition


title: Conserved long-range base pairings associated with pre-mRNA processing of human protein-coding genes

abstract: The ability of nucleic acids to form double-stranded structures is essential for all living systems on Earth. While DNA employs it for genome replication, RNA molecules fold into complicated secondary and tertiary structures. Current knowledge on functional RNA structures in human protein-coding genes is focused on locally-occurring base pairs. However, chemical crosslinking and proximity ligation experiments have demonstrated that long-range RNA structures are highly abundant. Here, we present the most complete to-date catalog of conserved long-range RNA structures in the human transcriptome, which consists of 916,360 pairs of conserved complementary regions (PCCRs). PCCRs tend to occur within introns proximally to splice sites, suppress intervening exons, circumscribe circular RNAs, and exert an obstructive effect on cryptic and inactive splice sites. The double-stranded structure of PCCRs is supported by a significant decrease of icSHAPE nucleotide accessibility, high abundance of A-to-I RNA editing sites, and frequent occurrence of forked eCLIP peaks nearby. Introns with PCCRs show a distinct splicing pattern in response to RNA Pol II slowdown suggesting that splicing is widely affected by co-transcriptional RNA folding. Additionally, transcript starts and ends are strongly enriched in regions between complementary parts of PCCRs, leading to an intriguing hypothesis that RNA folding coupled with splicing could mediate co-transcriptional suppression of premature cleavage and polyadenylation events. PCCR detection procedure is highly sensitive with respect to bona fide validated RNA structures at the expense of having a high false positive rate, which cannot be reduced without loss of sensitivity. The catalog of PCCRs is visualized through a UCSC Genome Browser track hub.


title: Sampling bias exaggerates a textbook example of a trophic cascade

abstract: Understanding how ecosystems respond to the loss and recovery of large predators is a major challenge because these free-living systems are difficult to sample properly. We show how an accepted practice of nonrandom sampling has confounded this understanding in a textbook system (Yellowstone National Park) where carnivore [Canis lupus (wolf)] recovery is often associated with a trophic cascade involving changes in herbivore [Cervus canadensis (elk)] behavior and density that promote plant regeneration. Long-term data indicate that a customary practice of sampling only the tallest young plants overestimated regeneration of overstory aspen (Populus tremuloides) by a factor of 3-8 compared to random sampling. Sampling only the tallest young plants favored plants taller than the preferred browsing height of elk and overlooked non-regenerating aspen stands. Our results demonstrate how seemingly minor departures from principled sampling can generate substantial misunderstandings about the strength of trophic cascades in response to large predator recovery.

category: ecology


title: Integrated neural dynamics for behavioral decisions and attentional control in the prefrontal cortex

abstract:
Complex neural dynamics in the prefrontal cortex contribute to context-dependent decisions and attentional competition. To analyze these dynamics, we apply demixed principal component analysis to activity of a primate prefrontal cell sample recorded in a cued target detection task. The results track dynamics of cue and object coding, feeding into movements along a target present-absent decision axis in a low-dimensional subspace of population activity. For a single stimulus, object and cue coding are seen mainly in the contralateral hemisphere. Later, a developing decision code in both hemispheres may reflect interhemispheric communication. With a target in one hemifield and a competing nontarget in the other, each hemisphere initially encodes the contralateral object, but finally, decision coding is dominated by the task-relevant target. These findings further suggest that exchange of information between hemispheres plays a key role when attentional competition resolves. Tracking complex neural events in a low-dimensional activity subspace illuminates integration of neural codes towards task-appropriate behavior, comprising a building block of learned task structure in the prefrontal cortex.

AUTHOR SUMMARYFlexible adaptive processing of information is integral for everyday goal-directed behavior. To unravel dynamic representation of task-relevant information, we analyzed population activity of a primate prefrontal cell sample in a cued target detection task. In a low-dimensional neural subspace, with separate axes for cue, object identity and decision, trajectories showed initial coding of cue and object in the contralateral hemisphere, followed by coding of the behavioral decision across both hemispheres. With target and nontarget stimuli in opposite hemifields, the data suggest initial coding of the contralateral object in each hemisphere. Object coding is then rapidly shut off for the nontarget, and followed by bilateral coding of the target decision. The results bring detailed insight into task structure and information flow within and between the two frontal lobes as a decision is made and attentional competition is resolved.


title: Bayesian combination of mechanistic modeling and machine learning (BaM3): improving personalized tumor growth predictions

abstract: In clinical practice, a plethora of medical examinations are conducted to assess the state of a patients pathology producing a variety of clinical data. However, exploiting these data faces the following challenges: (C1) we lack the knowledge of the mechanisms involved in regulating these data variables, and (C2) data collection is sparse in time since it relies on patients clinical presentation. (C1) implies that only a small subset of the relevant variables can be modeled by virtue of mathematical modeling. This limitation allows models to be effective in analyzing the qualitative dynamics of the system, but limits their predictive accuracy. On the other hand, statistical learning methods are well-suited for quantitative reproduction of data, but they do not provide mechanistic understanding of the investigated problem. Moreover, due to (C2) any algorithm is challenged in learning the corresponding disease dynamics. Herein, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning (BaM3), aiming at improving individualized predictions by addressing the aforementioned challenges. As a proof of concept, we evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two major clinical outputs, namely tumor burden and infiltration. The BaM3 method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation. In addition, we test the proposed methodology in two settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, BaM3 predictions show excellent agreement with reported clinical data.


title: Independent somatic evolution underlies clustered neuroendocrine tumors in the human small intestine

abstract: BackgroundSmall intestine neuroendocrine tumor (SI-NET), the most common cancer of the small bowel, often displays a curious multifocal phenotype with several intestinal tumors centered around a regional lymph node metastasis. Although SI-NET patients often present with metastatic disease at the time of diagnosis, there is an unusual absence of somatic driver mutations explaining tumor initiation and metastatic spread. The evolutionary trajectories that underlie multifocal SI-NET lesions could provide insight into the underlying tumor biology, but this question remains unresolved. Here, we determined the complete genome sequences of 65 tumor and tissue samples from 11 patients with multifocal SI-NET, allowing for elucidation of phylogenetic relationships between tumors within individual patients. Intra-individual comparisons of whole genome sequences revealed a lack of shared somatic single-nucleotide variants and copy number alterations among the sampled intestinal lesions, supporting that they were of independent clonal origin. Furthermore, each metastasis originated from a single intestinal tumor, and in three of the patients, two independent tumors had metastasized. We conclude that primary multifocal SI-NETs generally arise from clonally independent cells, suggesting a contribution from of a cancer-priming local factor.


title: The Impact of Graph Construction Scheme and Community Detection Algorithm on the Reliability of Community and Hub Identification in Structural Brain Networks

abstract:
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organization of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test-retest data from the Human Connectome Project), the repeatability of 33 community detection algorithms, each paired with 7 different graph construction schemes was assessed.

Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can be belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines 9 white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on optimal pairing of community detection algorithm and graph construction scheme.


title: Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

abstract: A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing the mutational processes that gave rise to them. Existing phylogenetic tree building models do not scale to the tens of thousands of high resolution genomes achievable with current scWGS methods. We constructed a phylogenetic model and associated Bayesian inference procedure, sitka, specifically for scWGS data. The method is based on a novel phylogenetic encoding of copy number (CN) data, the sitka transformation, that simplifies the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. We demonstrate our method on three single cell datasets, including a novel PDX series, and analyse the topological properties of the inferred trees. Sitka is freely available at https://github.com/UBC-Stat-ML/sitkatree.git.


title: Optimizing experimental design for genome sequencing and assembly with Oxford Nanopore Technologies

abstract:
BackgroundHigh quality reference genome sequences are the core of modern genomics. Oxford Nanopore Technologies (ONT) produces inexpensive DNA sequences in excess of 100,000 nucleotides but high error rates make sequence assembly and analysis a non-trivial problem as genome size and complexity increases. To date there has been no comprehensive attempt to generate robust experimental design for ONT genome sequencing and assembly. In this study, we simulate ONT and Illumina DNA sequence reads for the model organisms Escherichia coli, Caenorhabditis elegans, Arabidopsis thaliana, and Drosophila melanogaster and assemble with Canu, Flye, and MaSuRCA software to quantify the influence of sequencing coverage and assembly approach. Heterozygosity in outbred eukaryotes is a common problem for genome assembly. We show broad applicability of our methods using real ONT data generated for four strains of the highly heterozygous nematode Caenorhabditis remanei and C. latens.

ResultsO_LIONT libraries have a unique error structure and high sequence depth is necessary to assemble contiguous genome sequences.
C_LIO_LIAs sequence depth increases errors accumulate and assembly statistics plateau.
C_LIO_LIHigh-quality assembled sequences require a combination of experimental techniques that increase sequence read length and computational protocols that reduce error through correction, read selection and  polishing with higher accuracy short sequence reads.
C_LIO_LIOur robust experimental design results in highly contiguous and accurate genome assemblies for the four strains of C. remanei and C. latens.
C_LI

ConclusionsONT sequencing is inexpensive and accessible but the technologys error structure requires robust experimental design. Our quantitative results will be helpful for a broad array of researchers seeking guidance for de novo assembly projects.


title: The changes in the p53 protein across the animal kingdom pointing to its involvement in longevity

abstract: Recently, the quest for the mythical fountain of youth has turned into extensive research programs aiming to extend the healthy lifespan in humans. Despite advances in our understanding of the aging process, the surprisingly extended lifespan and cancer resistance of some animal species remains unexplained. The p53 protein plays a crucial role in tumor suppression and in tissue homeostasis and aging. Long-lived, cancer-free African elephants, have 20 copies of TP53 gene including 19 retrogenes (38 alleles) which are partially active, whereas humans possess only one copy of TP53 and have an estimated cancer mortality of 11-25%. The mechanism through which p53 contributes to the resolution of the Petos paradox in the Animalia remains vague. Thus, in this work, we took advantage of the available datasets and inspected the p53 amino acid sequence of phylogenetically related organisms that show variations in the lifespan. We discovered new correlations between specific amino acid deviations in p53 and the lifespans across different animal species. We found that species with extended lifespan have certain characteristic amino acid substitutions in the p53 DNA binding domain that alter its function as depicted from the Phenotypic Annotation of p53 Mutations, using PROVEAN tool or SWISS-MODEL workflow. Our findings show a direct association between specific amino acid residues in p53 protein, changes in p53 functionality and the extended animal lifespan, and further highlight the importance of p53 protein in aging.


title: Identification of 370 genetic loci for age at first sex and birth linked to externalising behaviour

abstract: Age at first sexual intercourse (AFS) and age at first birth (AFB) have implications for health and evolutionary fitness. In the largest genome-wide association study to date (AFS, N=387,338; AFB, N=542,901), we identify 370 independent signals, 11 sex-specific, with a 5-6% polygenic score (PGS) prediction. Heritability of AFB shifted from 9% [CI=4-14] for women born in 1940 to 22% [CI=19-25] in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility, and spermatid differentiation. Polycystic Ovarian Syndrome leads to later AFB, linking with infertility. Late AFB is protective against later-life disease and associated with parental longevity. Higher childhood socioeconomic circumstances and those in the highest PGS decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, for understanding longevity, and guiding experimentation into mechanisms of infertility.

category: genetics


title: Development of a vaccine against the newly emerging COVID-19 virus based on the receptor binding domain displayed on virus-like particles

abstract: The ongoing coronavirus COVID-19 pandemic is caused by a new coronavirus (SARS-CoV-2) with its origin in the city of Wuhan in China. From there it has been rapidly spreading to many cities inside and outside China. Nowadays more than 33 millions with deaths surpassing 1 million have been recorded worldwide thus representing a major health issue. Rapid development of a protective vaccine against COVID-19 is therefore of paramount importance. Here we demonstrated that recombinantly expressed receptor binding domain (RBD) of the spike protein homologous to SARS binds to ACE2, the viral receptor. Higly repetitive display of RBD on immunologically optimized virus-like particles derived from cucumber mosaic virus (CuMVTT) resulted in a vaccine candidate that induced high levels of specific antibodies in mice which were able to block binding of spike protein to ACE2 and potently neutralized COVID-19 virus in vitro.

category: immunology


title: Reclassification of seven honey bee symbiont strains as Bombella apis

abstract: Honey bees are important pollinators of many major crops and add billions of dollars annually to the US economy through their services. Recent declines in the health of the honey bee have startled researchers and lay people alike as honey bees are agricultures most important pollinator. One factor that may influence colony health is the microbial community. Although honey bee worker guts have a characteristic community of bee-specific microbes, the honey bee queen digestive tracts are colonized predominantly by a single acetic acid bacterium tentatively named "Parasaccharibacter apium". This bacterium is related to flower-associated microbes such as Saccharibacter floricola, and initial phylogenetic analyses placed it as sister to these environmental bacteria. We used a combination of phylogenetic and sequence identity methods to better resolve evolutionary relationships among "P. apium", strains in the genus Saccharibacter, and strains in the closely related genus Bombella. Interestingly, measures of genome-wide average nucleotide identity and aligned fraction, coupled with phylogenetic placement, indicate that many strains labeled as "P. apium" and Saccharibacter sp. are all the same species as Bombella apis. We propose reclassifying these strains as Bombella apis and outline the data supporting that classification below.


title: Coding and regulatory variants affect serum protein levels and common disease

abstract: Circulating proteins are prognostic for human outcomes including cancer, heart failure, brain trauma and brain amyloid plaque burden. A deep serum proteome survey recently revealed close associations of serum protein networks and common diseases. The present study reveals unprecedented number of individual serum proteins that overlap genetic signatures of diseases emanating from different tissues of the body. Here, 54,469 low-frequency and common exome-array variants were compared with 4782 protein measurements in the serum of 5343 individuals of the deeply annotated AGES Reykjavik cohort. Using a study-wide significant threshold, 2019 independent exome array variants affecting levels of 2135 serum proteins were identified. These variants overlapped genetic loci for hundreds of complex disease traits, emphasizing the emerging role for serum proteins as biomarkers of and potential causative agents of multiple diseases.


title: Neural Correlates of Value Are Intrinsically History Dependent

abstract: The authors have withdrawn their manuscript since they no longer stand by the results reported in this preprint. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.


title: A combination of mRNA features influence the efficiency of leaderless mRNA translation initiation

abstract: Bacterial translation is thought to initiate by base-pairing of the 16S rRNA and the Shine-Dalgarno sequence in the mRNAs 5UTR. However, transcriptomics has revealed that leaderless mRNAs, which completely lack any 5UTR, are broadly distributed across bacteria and can initiate translation in the absence of the Shine-Dalgarno sequence. To investigate the mechanism of leaderless mRNA translation initiation, synthetic in vivo translation reporters were designed that systematically tested the effects of start codon accessibility, leader length, and start codon identity on leaderless mRNA translation initiation. Using this data, a simple computational model was built based on the combinatorial relationship of these mRNA features which can accurately classify leaderless mRNAs and predict the translation initiation efficiency of leaderless mRNAs. Thus, start codon accessibility, leader length, and start codon identity combine to define leaderless mRNA translation initiation in bacteria.


title: Temporal correlation between oscillating force dipoles drives 3D single cell migration

abstract: Directional cell locomotion requires symmetry breaking between the front and rear of the cell. How this manifests itself for cells moving in physiological 3D matrices is often elusive. We take inspiration from the scallop theorem proposed by Purcell for micro-swimmers in Newtonian fluids: self-propelled objects undergoing persistent motion at low Reynolds number must follow a cycle of shape changes that breaks temporal symmetry. We report similar concepts for cells crawling in 3D. We quantified cell motion using a combination of 3D live cell imaging, visualisation of the matrix displacement and a minimal model with multipolar expansion. We show that cells embedded in 3D matrix form myosin-driven force dipoles at both sides of the nucleus, that locally and periodically pinch the matrix. The existence of a phase shift between the two dipoles is required for directed cell motion which manifests itself in cycles in the dipole-quadrupole diagram, a formal equivalence to the Purcell cycle. We confirm this mechanism by triggering local dipolar contractions with a laser, which leads to directed motion. Our study reveals that the cell controls its motility by synchronising dipolar forces distributed at front and back. This result opens new strategies to externally control cell motion as well as for the design of micro-crawlers.

category: biophysics


title: A Phylogenetic Approach to Inferring the Order in Which Mutations Arise during Cancer Progression

abstract: SO_SCPLOWUMMARYC_SCPLOWAlthough the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing provides a unique opportunity to examine the mutation order during cancer progression. However, the errors associated with single-cell sequencing complicate this task. We propose a new method for inferring the order in which somatic mutations arise within a tumor using noisy single-cell sequencing data that incorporates the errors that arise from the data collection process. Using simulation, we show that our method outperforms existing methods for identifying mutation order in most cases, especially when the number of cells is large. Our method also provides a means to quantify the uncertainty in the inferred mutation order along a fixed phylogeny. We apply our method to empirical data from colorectal and prostate cancer patients.


title: Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

abstract: Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.


title: A hybrid open-top light-sheet microscope for multi-scale imaging of cleared tissues

abstract: Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a user-friendly system that can address imaging applications with varied requirements in terms of resolution (mesoscopic to sub-micrometer), sample geometry (size, shape, and number), and compatibility with tissue-clearing protocols and sample holders of various refractive indices. We present a  hybrid system that combines a novel non-orthogonal dual-objective and conventional (orthogonal) open-top light-sheet architecture for versatile multi-scale volumetric imaging.

category: bioengineering


title: Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders

abstract: Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in random effect models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting solution is poorly characterized and the efficiency of the method may be suboptimal. Here we prove analytically that maximum-likelihood latent variables can always be chosen orthogonal to the known confounding factors, in other words, that maximum-likelihood latent variables explain sample covariances not already explained by known factors. Based on this result we propose a restricted maximum-likelihood method which estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors, and show that this reduces to probabilistic PCA on that subspace. The method then estimates the variance-covariance parameters by maximizing the remaining terms in the likelihood function given the latent variables, using a newly derived analytic solution for this problem. Compared to gradient-based optimizers, our method attains greater or equal likelihood values, can be computed using standard matrix operations, results in latent factors that dont overlap with any known factors, and has a runtime reduced by several orders of magnitude. Hence the restricted maximum-likelihood method facilitates the application of random effect modelling strategies for learning latent variance components to much larger gene expression datasets than possible with current methods.


title: Virome composition in marine fish revealed by meta-transcriptomics

abstract: Revealing the determinants of virome composition is central to placing disease emergence in a broader evolutionary context. Fish are the most species-rich group of vertebrates and so provide an ideal model system to study the factors that shape virome compositions and their evolution. We characterised the viromes of 19 wild-caught species of marine fish using total RNA sequencing (meta-transcriptomics) combined with analyses of sequence and protein structural homology to identify divergent viruses that often evade characterisation. From this, we identified 25 new vertebrate-associated viruses and a further 22 viruses likely associated with fish diet or their microbiomes. The vertebrate-associated viruses identified here included the first fish virus in the Matonaviridae (single-strand, negative-sense RNA virus). Other viruses fell within the Astroviridae, Picornaviridae, Arenaviridae, Reoviridae, Hepadnaviridae, Paramyxoviridae, Rhabdoviridae, Hantaviridae, Filoviridae and Flaviviridae and were sometimes phylogenetically distinct from known fish viruses. We also show how key metrics of virome composition - viral richness, abundance and diversity - can be analysed along with host ecological and biological factors as a means to understand virus ecology. Accordingly, these data suggest that that the vertebrate-associated viromes of the fish sampled here are predominantly shaped by the phylogenetic history (i.e. taxonomic order) of their hosts, along with several biological factors including water temperature, habitat depth, community diversity and swimming behaviour. No such correlations were found for viruses associated with porifera, molluscs, arthropods, fungi and algae, that are unlikely to replicate in fish hosts. Overall, these data indicate that fish harbour particularly large and complex viromes and the vast majority of fish viromes are undescribed.


title: Evolution and genetic basis of the plant-penetrating ovipositor, a key adaptation in the transition to herbivory within the Drosophilidae

abstract: Herbivorous insects are extraordinarily diverse, yet are found in only one-third of insect orders. This skew may result from barriers to plant colonization, coupled with phylogenetic constraint on plant-colonizing adaptations. Physical barriers have been surmounted through the evolution of key morphological innovations, such as the plant-penetrating ovipositor. Despite their significance, the evolution and genetic basis of such innovations have not been well studied. Ovipositors densely lined with hard bristles have evolved repeatedly in herbivorous lineages within the Drosophilidae. Here, we focus on the evolution of this trait in Scaptomyza, an herbivorous radiation nested in a microbe-feeding clade, sister to Hawaiian Drosophila. Our phylogenetic approach revealed that ovipositor bristle number increased as herbivory evolved. We then dissected the genomic architecture of variation in ovipositor bristle number within S. flava through a genome wide association study. Top associated variants were enriched for transcriptional repressors, and the strongest associations included genes contributing to peripheral nervous system development. Genotyping individual flies replicated the association at a variant upstream of Gi, a neural development gene, contributing to a gain of 0.58 bristles/major allele. These results suggest that regulatory variation involving conserved developmental genes contributes to a key morphological adaptation required for plant colonization.

category: evolutionary biology


title: Screening for Potential Interaction Partners with Surface Plasmon Resonance Imaging Coupled to MALDI Mass Spectrometry

abstract:
We coupled SPR imaging (SPRi) with matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) to identify new potential RNA binders. Here, we improve this powerful method, especially by optimizing the proteolytic digestion (type of reducing agent, its concentration, and incubation time), to work with complex mixtures, specifically a lysate of the rough mitochondrial fraction from yeast. The advantages of this hyphenated method compared to column-based or separate analyses are (i) rapid and direct visual readout from the SPRi array, (ii) possibility of high-throughput analysis of different interactions in parallel, (iii) high sensitivity, and (iv) no sample loss or contamination due to elution or micro-recovery procedures. The model system used is a catalytically active RNA (group IIB intron from Saccharomyces cerevisiae, Sc.ai5{gamma}) and its cofactor Mss116. The protein supports the RNA folding process and thereby the subsequent excision of the intronic RNA from the coding part. Using the novel approach of coupling SPR with MALDI MS, we report the identification of potential RNA-binding proteins from a crude yeast mitochondrial lysate in a non-targeted approach. Our results show that proteins other than the well-known cofactor Mss116 interact with Sc.ai5{gamma} (Dbp8, Prp8, Mrp13, and Cullin-3), suggesting that the intron folding and splicing are regulated by more than one cofactor in vivo.

Graphical abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/082776v3_ufig1.gif" ALT="Figure 1">
View larger version (20K):
org.highwire.dtl.DTLVardef@50a09borg.highwire.dtl.DTLVardef@1602253org.highwire.dtl.DTLVardef@6d9cc7org.highwire.dtl.DTLVardef@4ff0f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG Author contributionUlrike Anders and Maya Gulotti-Georgieva contributed equally to this work.


title: A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes

abstract: Over the past decades, optimization of media formulation and feeding strategies have fueled a many-fold improvement in CHO-based biopharmaceutical production. While Design of Experiments (DOE) and media screens have led to many advances, genome editing offers another avenue for enhancing cell metabolism and bioproduction. However the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we developed a comprehensive pooled CRISPR screen for CHO cell metabolism, including [~]16,000 gRNAs against [~]2500 metabolic enzymes and regulators. We demonstrated the value of this screen by identifying a glutamine response network in CHO cells. Glutamine is particularly important since it is often substantially over-fed to drive increased TCA cycle flux but can lead to accumulation of toxic ammonia. Within the glutamine-response network, the deletion of a novel and poorly characterized lipase, Abhd11, was found to substantially increase growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait.


title: Cast away on Mindoro island: population dynamics of the critically endangered tamaraw (Bubalus mindorensis) at Mounts Iglit-Baco Natural Park

abstract: Endangered species, despite often living at low population densities, may undergo unexpected density-dependent feedbacks in the case of successful recovery or marked reduction in range. Because density-dependence dynamics can increase risk of extinction, these effects can hamper conservation efforts. In this study, we analyse the dynamics of the largest population of the tamaraw (Bubalus mindorensis), a critically endangered species endemic to the Mindoro island, Philippines. The population is located within a < 3,000 ha area in Mounts Iglit-Baco Natural Park, with limited expansion possibilities. We took advantage of a 21 year time series of tamaraw counts to estimate annual population growth rate and possible density-dependence, accounting for sampling errors in the counts. The tamaraw population has been increasing in size at an average rate of +5% per year, as would be expected given its protected status by law. Population growth showed strong spatial structuring, with a population growth close to +10% in the core area of protection, and a reduction of abundance of - 5% at the periphery of its range, inside the protected area. This range constriction is concerning because our best population dynamic model suggests significant negative density-dependence (Bayes factor = 0.9). The contraction of tamaraw range is likely caused by anthropogenic pressures forcing herbivores to live at relatively high densities in the core zone where protection is most effective. Our study highlights the fact that, despite the continuous population growth over the last two decades, the long-term viability of the Mounts Iglit-Baco Natural Park tamaraw population remains uncertain.


title: Cast away on Mindoro island: could density dependence threaten tamaraw (Bubalus mindorensis) on the long term?


title: Microbial helpers allow cyanobacteria to thrive in ferruginous waters

abstract: The Great Oxidation Event (GOE) was a rapid accumulation of oxygen in the atmosphere as a result of the photosynthetic activity of cyanobacteria. This accumulation reflected the pervasiveness of O2 on the planets surface, indicating that cyanobacteria had become ecologically successful in Archean oceans. Micromolar concentrations of Fe2+ in Archean oceans would have reacted with hydrogen peroxide, a byproduct of oxygenic photosynthesis, to produce hydroxyl radicals, which cause cellular damage. Yet cyanobacteria colonized Archean oceans extensively enough to oxygenate the atmosphere, which likely required protection mechanisms against the negative impacts of hydroxyl radical production in Fe2+-rich seas. We identify several factors that could have acted to protect early cyanobacteria from the impacts of hydroxyl radical production and hypothesize that microbial cooperation may have played an important role in protecting cyanobacteria from Fe2+ toxicity before the GOE. We found that several strains of facultative anaerobic heterotrophic bacteria (Shewanella) with ROS defense mechanisms increase the fitness of cyanobacteria (Synechococcus) in ferruginous waters. Shewanella species with manganese transporters provided the most protection. Our results suggest that a tightly regulated response to prevent Fe2+ toxicity could have been important for the colonization of ancient ferruginous oceans, particularly in the presence of high manganese concentrations, and may expand the upper bound for tolerable Fe2+ concentrations for cyanobacteria.


title: The phylogenetic and global distribution of bacterial polyhydroxyalkanoate bioplastic degrading genes

abstract:
Polyhydroxyalkanoates (PHAs) are a family of microbially-made polyesters commercialized as biodegradable plastics. PHA production rates are predicted to increase as concerns around environmental plastic contamination and limited fossil fuel resources have increased the importance of biodegradable and bio-based plastic alternatives. Microbially-produced PHA depolymerases are the key enzymes mediating PHA biodegradation, but only a few PHA depolymerases have been well-characterized and screens employing metagenomic sequence data are lacking. Here, we used 3,078 metagenomes to analyze the distribution of PHA depolymerases in microbial communities from diverse aquatic, terrestrial and waste management systems. We significantly expand the recognized diversity of this protein family by screening 1,914 Gb of sequence data and identifying 13,869 putative PHA depolymerases in 1,295 metagenomes. Our results indicate that PHA depolymerases are unevenly distributed across environments. We predicted the highest frequency of PHA depolymerases in wastewater systems and the lowest in marine and thermal springs. In tandem, we screened 5,290 metagenome-assembled genomes to describe the phylogenetic distribution of PHA depolymerases, which is substantially broader compared to current cultured representatives. The Proteobacteria and Bacteroidota are key lineages encoding PHA depolymerases, but PHA depolymerases were predicted from members of the Bdellovibrionota, Methylomirabilota, Actinobacteriota, Firmicutes, Spirochaetota, Desulfobacterota, Myxococcota and Planctomycetota.

Originality/Significance StatementBiodegradable plastics like polyhydroxyalkanoates (PHAs) are a hot topic, following ubiquitous environmental plastic contamination, government bans on single-use plastics, and a growing need for sustainable alternatives to petroleum-based plastics. Understanding the microbial conversion of PHAs in the environment and finding biomolecular tools that can act on PHAs is increasingly important as PHAs grow in popularity. In this study, we screened thousands of metagenomes and metagenome-assembled genomes (MAGs) to substantially increase the recognized diversity of PHA depolymerases, the key enzymes mediating PHA biodegradation. We use datasets from seven continents to provide a global summary of the distribution of PHA depolymerase genes in natural environments and waste-management systems. In tandem, we increase the number of described phylum-level lineages with PHA biodegradation potential. This work contributes a new understanding of the phylogenetic and environmental distribution of PHA depolymerases.


title: Condensation of pericentrin proteins in human cells illuminates phase separation in centrosome assembly

abstract: AbstractAt the onset of mitosis, centrosomes expand the pericentriolar material (PCM) to maximize their microtubule-organizing activity. This step, termed centrosome maturation, ensures proper spindle organization and faithful chromosome segregation. However, as the centrosome expands, how PCM proteins are recruited and held together without membrane enclosure remains elusive. We found that endogenously expressed pericentrin (PCNT), a conserved PCM scaffold protein, condenses into dynamic granules during late G2/early mitosis before incorporating into mitotic centrosomes. Furthermore, the N-terminal portion of PCNT--enriched with conserved coiled-coils (CCs) and low-complexity regions (LCRs)--phase separates into dynamic condensates that selectively recruit PCM proteins and nucleate microtubules in cells. We propose that CCs and LCRs, two prevalent sequence features in the centrosomal proteome, are preserved under evolutionary pressure in part to mediate liquid-liquid phase separation, a process that bestows upon the centrosome distinct properties critical for its assembly and functions.


title: Differential neural circuitry behind autism subtypes with imbalanced social-communicative and restricted repetitive behavior symptoms

abstract: Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC>RRB and visual association circuitry in SC=RRB. The SC=RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.


title: E2F4 as a single multifactorial target against Alzheimer's disease

abstract: Alzheimers disease (AD) has a multifactorial etiology, which requires a single multi-target approach for an efficient treatment. We have focused on E2F4, a transcription factor that regulates cell quiescence and tissue homeostasis, controls gene networks affected in AD, and is upregulated in the brain of Alzheimers patients and of APPswe/PS1dE9 and 5xFAD transgenic mice. E2F4 contains an evolutionarily-conserved Thr-motif that, when phosphorylated, modulates its activity, thus constituting a potential target for intervention. Here we show that neuronal expression in 5xFAD mice of a dominant negative form of E2F4 lacking this Thr-motif (E2F4DN) potentiates a transcriptional program consistent with the attenuation of the immune response and global brain homeostasis. This correlates with reduced microgliosis and astrogliosis, modulation of A{beta} proteostasis, and blockade of neuronal tetraploidization. Moreover, E2F4DN prevents cognitive impairment and body weight loss, a known somatic alteration associated with AD. Our finding is relevant for AD, since E2F4 is expressed in cortical neurons from Alzheimer patients in association with Thr-specific phosphorylation, as evidenced by an anti-E2F4/anti-phosphoThr proximity ligation assay. We propose E2F4DN-based gene therapy as a promising multifactorial approach against AD.

category: pathology


title: Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data

abstract: The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.


title: Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

abstract: Recent advances in non-linear computational and dynamical modelling have opened up the possibility to param etrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individuals empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.


title: Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development  study

abstract: The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.


title: Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

abstract: Accurate and efficient detection of copy number variants (CNVs) is of critical importance due to their significant association with complex genetic diseases. Although algorithms that use whole genome sequencing (WGS) data provide stable results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data is cost efficient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent from (i) sequencing technology, (ii) exome capture kit and (iii) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.


title: Measuring context dependency in birdsong using artificial neural networks

abstract: Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial information to understand the mechanism for complex sequential behaviors. Birdsongs serve as a representative model for studying the context dependency in sequential signals produced by non-human animals, while previous reports were upper-bounded by methodological limitations. Here, we newly estimated the context dependency in birdsongs in a more scalable way using a modern neural-network-based language model whose accessible context length is sufficiently long. The detected context dependency was beyond the order of traditional Markovian models of birdsong, but was consistent with previous experimental investigations. We also studied the relation between the assumed/auto-detected vocabulary size of birdsong (i.e., fine-vs. coarse-grained syllable classifications) and the context dependency. It turned out that the larger vocabulary (or the more fine-grained classification) is assumed, the shorter context dependency is detected.


title: Heparan sulfate proteoglycans as attachment factor for SARS-CoV-2

abstract: Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is causing an unprecedented global pandemic demanding the urgent development of therapeutic strategies. Microarray binding experiments using an extensive heparan sulfate (HS) oligosaccharide library showed that the receptor binding domain (RBD) of the spike of SARS-CoV-2 can bind HS in a length-and sequence-dependent manner. Hexa- and octa-saccharides composed of IdoA2S-GlcNS6S repeating units were identified as optimal ligands. Surface plasma resonance (SPR) showed the SARS-CoV-2 spike protein binds with much higher affinity to heparin (KD = 55 nM) compared to the RBD (KD = 1 M) alone. We also found that heparin does not interfere in angiotensin-converting enzyme 2 (ACE2) binding or proteolytic processing of the spike. Our data supports a model in which HS functions as the point of initial attachment for SARS-CoV-2 infection. Tissue staining studies using biologically relevant tissues indicate that heparan sulfate proteoglycan (HSPG) is a critical attachment factor for the virus. Collectively, our results highlight the potential of using HS oligosaccharides as a therapeutic agent by inhibiting SARS-CoV-2 binding to target cells.

category: biochemistry


title: Prolonged impacts of past agriculture and overgrazing on soil fungal communities in restored forests

abstract: Soil fungi can help improve ecosystem restoration, yet our understanding of how they reassemble in degraded land is limited. Here, we studied fungal community structure using DNA metabarcoding in reforested sites following agricultural abandonment and overgrazing. Two treatments, namely  reforestation using different numbers of tree species and  deer exclusion, have been applied for multiple decades in the study sites. We found that local fungal richness (alpha diversity) and total fungal richness (gamma diversity) were 1.9-2.9 and 1.3-1.9 times greater, respectively, in reforested stands than in natural forests. These results were regardless of the number of tree species planted in the reforested stands. Conversely, reforested stands had a spatially homogenized community structure with relatively lower degrees of compositional dissimilarity among sites within each stand (beta diversity). These findings were attributable to lower environmental heterogeneity, stronger dispersal limitation, and a comparatively shorter time since the onset of community assembly in reforested stands. Deer exclosures had no detectable effect on fungal community structure. Overall, the agricultural legacy in fungal community structure appears to have persisted for decades, even under proactive restoration of aboveground vegetation. Direct human intervention belowground may therefore be necessary for the recovery of soil biota once altered.


title: The role of high cholesterol in age related COVID19 lethality.

abstract: Coronavirus disease 2019 (COVID19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originating in Wuhan, China in 2019. The disease is notably severe in elderly and those with underlying chronic conditions. A molecular mechanism that explains why the elderly are vulnerable and why children are resistant is largely unknown. Here we show loading cells with cholesterol from blood serum using the cholesterol transport protein apolipoprotein E (apoE) enhances the entry of pseudotyped SARS-CoV-2 and the infectivity of the virion. Super resolution imaging of the SARS-CoV-2 entry point with high cholesterol shows almost twice the total number of endocytic entry points. Cholesterol concomitantly traffics angiotensinogen converting enzyme (ACE2) to the endocytic entry site where SARS-CoV-2 presumably docks to efficiently exploit entry into the cell. Furthermore, in cells producing virus, cholesterol optimally positions furin for priming SARS-CoV-2, producing a more infectious virion with improved binding to the ACE2 receptor. In vivo, age and high fat diet induces cholesterol loading by up to 40% and trafficking of ACE2 to endocytic entry sites in lung tissue from mice. We propose a component of COVID19 severity based on tissue cholesterol level and the sensitivity of ACE2 and furin to cholesterol. Molecules that reduce cholesterol or disrupt ACE2 localization with viral entry points or furin localization in the producer cells, may reduce the severity of COVID19 in obese patients.


title: SARS-CoV-2 Spike Glycoprotein and ACE2 interaction reveals modulation of viral entry in wild and domestic animals

abstract:
BackgroundSARS-CoV-2 is a viral pathogen causing life-threatening disease in human. Interaction between spike protein of SARS-CoV-2 and ACE2 receptor on the cells is a potential factor in the infectivity of a host. The interaction of SARS-CoV-2 spike receptor-binding domain with its receptor - ACE2, in different hosts was evaluated to understand and predict viral entry. The protein and nucleotide sequences of ACE2 were initially compared across different species to identify key differences among them. The ACE2 receptor of various species was homology modeled (6LZG, 6M0J, and 6VW1 as a reference), and its binding ability to the spike ACE2 binding domain of SARS-CoV-2 was assessed. Initially, the spike binding parameters of ACE2 of known infected and uninfected species were compared with each Order (of animals) as a group. Finally, a logistic regression model vis-a-vis the spike binding parameters of ACE2 (considering data against 6LZG and 6M0J) was constructed to predict the probability of viral entry in different hosts.

ResultsPhylogeny and alignment comparison did not lead to any meaningful conclusion on viral entry in different hosts. Out of several spike binding parameters of ACE2, a significant difference between the known infected and uninfected species was observed for six parameters. However, these parameters did not specifically categorize the Orders (of animals) into infected or uninfected. The logistic regression model constructed revealed that in the mammalian class, most of the species of Carnivores, Artiodactyls, Perissodactyls, Pholidota, and Primates had high probability of viral entry. However, among the primates, African Elephant had low probability of viral entry. Among rodents, hamsters were highly probable for viral entry with rats and mice having a medium to low probability. Rabbits have a high probability of viral entry. In Birds, ducks have a very low probability, while chickens seemed to have medium probability and turkey showed the highest probability of viral entry.

ConclusionsMost of the species considered in this study showed high probability of viral entry. This study would prompt us to closely follow certain species of animals for determining pathogenic insult by SARS-CoV-2 and for determining their ability to act as a carrier and/or disseminator.


title: Using EEG Microstates to Examine Post-Encoding Quiet Rest and Subsequent Word-Pair Memory

abstract: Evidence suggests that the brain preferentially consolidates memories during "offline" periods, in which an individual is not performing a task and their attention is otherwise undirected, including spans of quiet, resting wakefulness. Moreover, research has demonstrated that factors such as the initial encoding strength of information influence which memories receive the greatest benefit. Recent studies have begun to investigate these periods of post-learning quiet rest using EEG microstate analysis to observe the electrical dynamics of the brain during these stretches of memory consolidation, specifically finding an increase in the amount of the canonical microstate D during a post-encoding rest period. Here, we implement an exploratory analysis to probe the activity of EEG microstates during a post-encoding session of quiet rest in order to scrutinize the impact of learning on microstate dynamics and to further understand the role these microstates play in the consolidation of memories. We examined 54 subjects (41 female) as they completed a word-pair memory task designed to use repetition to vary the initial encoding strength of the word-pair memories. In this study, we were able to replicate previous research in which there was a significant increase (p < .05) in the amount of microstate D (often associated with the dorsal attention network) during post-encoding rest. This change was accompanied by a significant decrease (p < .05) in the amount of microstate C (often associated with the default mode network). We also found preliminary evidence indicating a positive relationship between the amount of microstate D and improved memory for weakly encoded memories, which merits further exploration.


title: Binding partners regulate unfolding of myosin VI to activate the molecular motor

abstract: Myosin VI is the only minus-end actin motor and is coupled to various cellular processes ranging from endocytosis to transcription. This multi-potent nature is achieved through alternative isoform splicing and interactions with a network of binding partners. There is a complex interplay between isoforms and binding partners to regulate myosin VI. Here, we have compared the regulation of two myosin VI splice isoforms by two different binding partners. By combining biochemical and single-molecule approaches, we propose that myosin VI regulation follows a generic mechanism, independently of the spliced isoform and the binding partner involved. We describe how myosin VI adopts an autoinhibited backfolded state which is released by binding partners. This unfolding activates the motor, enhances actin binding and can subsequently trigger dimerization. We have further expanded our study by using single molecule imaging to investigate the impact of binding partners upon myosin VI molecular organisation and dynamics.