title: Segmentation-less, automated vascular vectorization robustly extracts neurovascular network statistics from in vivo two-photon images

abstract:
Recent advances in two-photon microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting a network graph and vector representations for vessels remain bottlenecks in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and low-complexity vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4x0.9x0.6 mm3 and 1.6x108 voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images.

Author summarySamuel Mihelic is a PhD candidate in the Biomedical Engineering Department at the University of Texas at Austin. He graduated from Oregon State University (Chemical Engineering BS, Mathematics BS). He hosts the GitHub repository for the code used in this article: https://github.com/UTFOIL/Vectorization-Public. His research interests are in-vivo neural microvascular image analysis, anatomy, and plasticity.

William Sikora graduated with a BS in Computational Biomedical Engineering from The University of Texas at Austin in May 2020. He is working with Dr. Yuan Yang and the Laureate Institute for Brain Research as a PhD student of Biomedical Engineering at the University of Oklahoma in Tulsa, researching the highly non-linear world of neural coupling and its link to common neurological pathologies such as stroke.

Ahmed Hassan is a graduate of the University of California, Los Angeles and the University of Texas at Austin with a Bachelor's degree in Microbiology, Immunology, and Molecular Genetics and an MSE/PhD in Biomedical Engineering. His graduate research was concentrated in imaging and instrumentation, and his interests include developing optical and laser systems for neuroimaging, image processing and reconstruction, and advanced image analysis.

Michael Williamson earned a BSc (Honours) in Neuroscience in 2016 from the University of Alberta, where he trained with Dr. Fred Colbourne. He is currently a doctoral student at the University of Texas at Austin working in the labs of Drs. Theresa Jones and Michael Drew.

Theresa Jones is a Professor in the Department of Psychology and Neuroscience at The University of Texas at Austin. Her laboratory studies plasticity of neural structure and synaptic connectivity following brain damage and injury.

Andrew K. Dunn is the Donald J. Douglass Centennial Professor of Engineering in the Department of Biomedical Engineering at The University of Texas at Austin and the Director of the Center for Emerging Imaging Technologies. His research focuses on the development of innovative optical imaging techniques for studying the brain.

category: bioengineering


title: Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations

abstract: Preliminary evidence indicates that occipito-temporal activation patterns for different visual stimuli are less distinct in older (OAs) than younger (YAs) adults, suggesting a dedifferentiation of visual representations with aging. Yet, it is unclear if this deficit (1) affects only sensory or also categorical aspects of representations during visual perception (perceptual representations), and (2) affects only perceptual or also mnemonic representations. To investigate these issues, we fMRI-scanned YAs and OAs viewing and then remembering visual scenes. First, using representational similarity analyses, we distinguished sensory vs. categorical features of perceptual representations. We found that, compared to YAs, sensory features in early visual cortex were less differentiated in OAs (i.e., age-related dedifferentiation), replicating previous research, whereas categorical features in anterior temporal lobe (ATL) were more differentiated in OAs. This is, to our knowledge, the first report of an age-related hyperdifferentiation. Second, we assessed the quality of mnemonic representations by measuring encoding-retrieval similarity (ERS) in activation patterns. We found that aging impaired mnemonic representations in early visual cortex and hippocampus but enhanced mnemonic representations in ATL. Thus, both perceptual and mnemonic representations in ATL were enhanced by aging. In sum, our findings suggest that aging impairs visual and mnemonic representations in posterior brain regions but enhances them in anterior regions.

category: neuroscience


title: Enhancing Mask Activity in Dopaminergic Neurons Extends Lifespan in Flies

abstract: Dopaminergic neurons (DANs) are essential modulators for brain functions involving memory formation, reward processing, and decision making. Here I demonstrate a novel and important function of the DANs in regulating aging and longevity. Overexpressing the putative scaffolding protein Mask in two small groups of DANs in flies can significantly extend the lifespan in flies and sustain adult locomotor and fecundity at old ages. Such a Mask-induced beneficial effect requires dopaminergic transmission but cannot be recapitulated by elevating dopamine production in the DANs. Independent activation of Gs in the same two groups of DANs via the drug-inducible DREADD system also extends fly lifespan, further indicating the connection of specific DANs to aging control. The Mask-induced lifespan extension appears to depend on the function of Mask to regulate microtubule (MT) stability. A structure-function analysis demonstrated that the ankyrin repeats domain in the Mask protein is both necessary for regulating MT stability (when expressed in muscles and motor neurons) and sufficient to prolong longevity (when expressed in the two groups of DANs). Furthermore, DAN-specific overexpression of Unc-104 or knockdown of p150Glued, two independent interventions previously shown to impact MTs dynamics, also extend lifespan in flies. Together these data demonstrated a novel DANs-dependent mechanism that, upon neuronal cell-type specific tuning of MT dynamics, modulates systemic aging and longevity in flies.


title: Emerin regulation of nuclear stiffness is required for fast amoeboid migration in confined environments

abstract:
When metastasizing, tumor cells must traverse environments with diverse physicochemical properties. Recently, the cell nucleus has emerged as a major regulator of the transition from mesenchymal to fast amoeboid (leader bleb-based) migration. Here, in melanoma cells, we demonstrate that increasing nuclear stiffness through elevating Lamin A, inhibits fast amoeboid migration. Importantly, nuclei may respond to force through stiffening. A key factor in this process is the inner nuclear membrane (INM) protein, emerin. Accordingly, we determined the role of emerin in regulating fast amoeboid migration. Strikingly, we found that both the up- and down-regulation of emerin results in an inhibition of fast amoeboid migration. However, when key Src phosphorylation sites were removed, up-regulation of emerin no longer inhibited fast amoeboid migration. Interestingly, in confined cells, Src activity was low, as measured by a Src biosensor. Thus, the fast amoeboid migration of melanoma cells depends on the precise calibration of emerin activity.

Summary StatementIn mechanically constrictive microenvironments, amoeboid migrating melanoma cells require emerin for the precise calibration of nuclear stiffness.

category: cell biology


title: Wnt-and Glutamate-receptors orchestrate stem cell dynamics and asymmetric cell division

abstract: The Wnt-pathway is part of a signalling network that regulates many aspects of cell biology. Recently we discovered crosstalk between AMPA/Kainate-type ionotropic glutamate receptors (iGluRs) and the Wnt-pathway during the initial Wnt3a-interaction at the cytonemes of embryonic stem cells (ESCs). Here, we demonstrate that this crosstalk persists throughout the Wnt3a-response in ESCs. Both AMPA- and Kainate-receptors regulate early Wnt3a-recruitment, dynamics on the cell membrane, and orientation of the spindle towards a Wnt3a-source at mitosis. AMPA-receptors specifically are required for segregating cell fate components during Wnt3a-mediated asymmetric cell division (ACD). Using Wnt-pathway component knockout lines, we determine that Wnt co-receptor Lrp6 has particular functionality over Lrp5 in cytoneme formation, and in facilitating ACD. Both Lrp5 and 6, alongside pathway effector {beta}-catenin act in concert to mediate the positioning of the dynamic interaction with, and spindle orientation to, a localized Wnt3a-source. Wnt-iGluR crosstalk may prove pervasive throughout embryonic and adult stem cell signalling.


title: Population genomic time series data of a natural population suggests adaptive tracking of environmental changes

abstract:
Natural populations are constantly exposed to fluctuating environmental changes that negatively affect their fitness in unpredictable ways. While theoretical models show the possibility of counteracting these environmental changes through rapid evolutionary adaptations, there have been few empirical studies demonstrating such adaptive tracking in natural populations.

Here, we analysed environmental data, fitness-related phenotyping and genomic time-series data sampled over three years from a natural Chironomus riparius (Diptera, Insecta) population to address this question. We show that the populations environment varied significantly on the time scale of the sampling in many selectively relevant dimensions, independently of each other. Similarly, phenotypic fitness components evolved significantly on the same temporal scale (mean 0.32 Haldanes), likewise independent from each other. The allele frequencies of 367,446 SNPs across the genome showed evidence of positive selection. Using temporal correlation of spatially coherent allele frequency changes revealed 35,574 haplotypes with more than one selected SNP. The mean selection coefficient for these haplotypes was 0.30 (s.d. = 0.68). The frequency changes of these haplotypes clustered in 46 different temporal patterns, indicating concerted, independent evolution of many polygenic traits. Nine of these patterns were strongly correlated with measured environmental variables.

Thus, our results suggest that the natural population of C. riparius tracks environmental change through rapid polygenic adaptation in many independent dimensions. This is further evidence that natural selection is pervasive at the genomic level and that evolutionary and ecological time scales may not differ at all, at least in some organisms.

category: evolutionary biology


title: Targeted control of toxin production by a mobile genetic element in Streptococcus pneumoniae.

abstract: Streptococcus pneumoniae is a major human pathogen that can cause severe invasive diseases such as pneumonia, septicaemia and meningitis. Young children are at a particularly high risk, with an estimated half a million deaths worldwide in those under five attributable to invasive pneumococcal disease each year. The cytolytic toxin pneumolysin (Ply) is a primary virulence factor for this bacterium, yet despite its key role in pathogenesis, immune evasion, and transmission, the regulation of Ply production is not well defined. Using a genome-wide association approach we identified a large number of potential affectors of Ply activity, including a gene acquired horizontally on the antibiotic resistance conferring Integrative and Conjugative Element (ICE) ICESp23FST81. This gene encodes a novel modular protein, ZomB, which has an N-terminal UvrD-like helicase domain followed by two Cas4-like domains with potent ATP-dependent nuclease activity. We found the regulatory effect of ZomB to be specific for the ply operon, potentially mediated by its high affinity for the BOX repeats encoded therein. Using a murine model of pneumococcal colonisation, we further demonstrate that a ZomB mutant strain colonises both the upper respiratory tract and lungs at higher levels when compared to the wild type strain. While the antibiotic resistance conferring aspects of ICESp23FST81 is often credited with contributing to the success of the S. pneumoniae lineages that acquire it, its ability to control the expression of a major virulence factor implicated in bacterial transmission is also likely to have played an important role.

category: microbiology


title: Polygenic local adaptation in metapopulations: a stochastic eco-evolutionary model

abstract: This paper analyses the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments.


title: Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

abstract: We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimers Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on newly generated myeloma single cell transcriptomics led to the identification of PHF19high myeloma cells associated with progression.

category: bioinformatics


title: Transcriptogram analysis reveals relationship between viral titer and gene set responses during Corona-virus infection.

abstract: To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. At the individual-gene level, EGR3 was significantly upregulated in infected cells. Similar activation in T-cells and fibroblasts in infected lung could explain the T-cell anergy and eventual fibrosis seen in SARS-CoV-1 infection. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models, and human-derived samples.


title: Rostrocaudal Patterning and Neural Crest Differentiation of Human Pre-Neural Spinal Cord Progenitors in vitro

abstract: The spinal cord emerges from a niche of neuromesodermal progenitors (NMPs) formed and maintained by Wnt/FGF signals at the posterior end of the embryo. NMPs can be generated from human pluripotent stem cells and hold promise for spinal cord replacement therapies. However, NMPs are transient, which complicates the full range production of rostrocaudal spinal cord identities in vitro. Here we report the generation of NMP-derived pre-neural progenitors (PNPs) with stem cell-like self-renewal capacity. PNPs maintain pre-spinal cord identity by co-expressing the transcription factors SOX2 and CDX2, and lose mesodermal potential by downregulating TBXT. For 7 to 10 passages PNPs divide to self-renew and to make trunk neural crest (NC), while gradually adopting a more posterior identity by activating colinear HOX gene expression. This HOX clock can be halted at the thoracic level for up to 30 passages by blocking the trunk-to-tail transition through GDF11-mediated signal inhibition.

category: developmental biology


title: Spatiotemporal expression of regulatory kinases directs the transition from mitosis to cellular morphogenesis

abstract: Embryogenesis depends on a tightly regulated balance between mitosis, differentiation, and morphogenesis. Understanding how the embryo uses a relatively small number of proteins to transition between growth and morphogenesis is a central question of developmental biology, but the mechanisms controlling mitosis and differentiation are considered to be fundamentally distinct. Here we show the mitotic kinase Polo, which regulates all steps of mitosis [1-3], also directs cellular morphogenesis after cell cycle exit. In mitotic cells, the Aurora kinases activate Polo to control a cytoskeletal regulatory module that directs cytokinesis [4-6]. We show that in the post-mitotic mesoderm, the control of Polo activity transitions from the Aurora kinases to the uncharacterized kinase Back Seat Driver (Bsd), where Bsd and Polo cooperate to regulate muscle morphogenesis. Polo and its effectors therefore direct mitosis and cellular morphogenesis, but the transition from growth to morphogenesis is determined by the spatiotemporal expression of upstream activating kinases.


title: Hippocampus-lateral septum circuitry mediates foraging-related spatial memory

abstract: Remembering the location of a food or water source is essential for survival. Here we demonstrate that spatial memory for food location is reflected in ventral hippocampus (HPCv) neuron activity and is impaired by HPCv lesion. HPCv mediation of foraging-related memory involves downstream lateral septum (LS) signaling, as both reversible and chronic disconnection of HPCv (field CA1) to LS signaling impairs spatial memory retention for the location of either food or water reinforcement. This neural pathway selectively encodes appetitive spatial memory, as HPCv-LS disconnection does not affect aversive reinforcement-based spatial memory in an escape task using the same apparatus. The selectivity of this pathway in promoting foraging-related memory is further supported by results showing that CA1v-LS disconnection does not affect food intake, anxiety-like behavior, locomotor activity, or social and olfactory-based appetitive learning. Fluorescent in situ hybridization reveals that LS neurons recruited during the appetitive spatial memory task are primarily GABAergic. Multisynaptic anterograde neural pathway tracing and immediate early gene mapping identify the lateral hypothalamic area as a functional downstream target of this pathway. Collective results reveal a novel neural circuit through which the hippocampus selectively mediates memory for the location of appetitive (food or water) but not aversive (escape) reinforcement.


title: Ventral hippocampus-lateral septum circuitry promotes foraging-related memory


title: Dissociation of task engagement and arousal effects in auditory cortex and midbrain

abstract: Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.


title: Syntactic representations in the human brain: beyond effort-based metrics

abstract: While studying semantics in the brain, neuroscientists use two approaches. One is to identify areas that are correlated with semantic processing load. Another is to find areas that are predicted by the semantic representation of the stimulus words. However, in the domain of syntax, most studies have focused only on identifying areas correlated with syntactic processing load. One possible reason for this discrepancy is that representing syntactic structure in an embedding space such that it can be used to model brain activity is a non-trivial computational problem. Another possible reason is that it is unclear if the low signal-to-noise ratio of neuroimaging tools such as functional Magnetic Resonance Imaging (fMRI) can allow us to reveal correlates of complex (and perhaps subtle) syntactic representations. In this study, we propose novel multi-dimensional features that encode information about the syntactic structure of sentences. Using these features and fMRI recordings of participants reading a natural text, we model the brain representation of syntax. First, we find that our syntactic structure-based features explain additional variance in the brain activity of various parts of the language system, even after controlling for complexity metrics that capture processing load. At the same time, we see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those processing semantics.


title: Can fMRI reveal the representation of syntactic structure in the brain?


title: Quantitative mapping of mRNA 3' ends in Pseudomonas aeruginosa reveals a pervasive role for premature 3' end formation in response to azithromycin

abstract: Pseudomonas aeruginosa produces serious chronic infections in hospitalized patients and immunocompromised individuals, including patients with cystic fibrosis. The molecular mechanisms by which P. aeruginosa responds to antibiotics and other stresses to promote persistent infections may provide new avenues for therapeutic intervention. Azithromycin (AZM), an antibiotic frequently used in cystic fibrosis treatment, is thought to improve clinical outcomes through a number of mechanisms including impaired biofilm growth and quorum sensing (QS). The mechanisms underlying the transcriptional response to AZM remain unclear. Here, we interrogated the P. aeruginosa transcriptional response to AZM using a fast, cost-effective genome-wide approach to quantitate RNA 3 ends (3pMap). We also identified hundreds of P. aeruginosa genes with high incidence of premature 3 end formation indicative of riboregulation in their transcript leaders using 3pMap. AZM treatment of planktonic and biofilm cultures alters the expression of hundreds of genes, including those involved in QS, biofilm formation, and virulence. Strikingly, most genes downregulated by AZM in biofilms had increased levels of intragenic 3 ends indicating premature transcription termination, transcriptional pausing, or accumulation of stable intermediates resulting from the action of nucleases. Reciprocally, AZM reduced premature intragenic 3 end termini in many upregulated genes. Most notably, reduced termination accompanied robust induction of obgE, a GTPase involved in persister formation in P. aeruginosa. Our results support a model in which AZM-induced changes in 3 end formation alter the expression of central regulators which in turn impairs the expression of QS, biofilm formation and stress response genes, while upregulating genes associated with persistence.


title: Continuum dynamics and statistical correction of compositional heterogeneity in multivalent IDP oligomers resolved by single-particle EM

abstract:
Multivalent intrinsically disordered protein (IDP) complexes are prevalent in biology and control diverse cellular functions, including tuning levels of transcription, coordinating cell-signaling events, and regulating the assembly and disassembly of complex macromolecular architectures. These systems pose a significant challenge to structural investigation, due to the continuum dynamics imparted by the IDP and compositional heterogeneity resulting from characteristic low-affinity interactions. Traditional single-particle electron microscopy (EM) is a powerful tool for visualizing IDP complexes. However, the IDPs themselves are typically "invisible" by EM, undermining methods of image analysis and structural interpretation. To overcome these challenges, we developed a pipeline for automated analysis of common  beads-on-a-string type of assemblies, composed of IDPs bound at multivalent sites to the ubiquitous ~20 kDa cross-linking hub protein LC8. This approach quantifies conformational and compositional heterogeneity on a single-particle basis, and statistically corrects spurious observations arising from random proximity of bound and unbound LC8. After careful validation of the methodology, the approach was applied to the nuclear pore IDP Nup159 and the transcription factor ASCIZ. The analysis unveiled significant compositional and conformational diversity in both systems that could not be obtained from traditional single particle EM class-averaging strategies, and shed new light on how these architectural properties contribute to their physiological roles in supramolecular assembly and transcriptional regulation. Ultimately, we expect that this approach may be adopted to many other intrinsically disordered systems that have evaded traditional methods of structural characterization.

Significance StatementIntrinsically disordered proteins (IDPs) or protein regions (IDRs) represent >30% of the human proteome, but mechanistically remain some of the most poorly understood classes of proteins in biology. This dearth in understanding stems from these very same intrinsic and dynamic properties, which make them difficult targets for quantitative and structural characterization. Here, we present an automated approach for extracting quantitative descriptions of conformational and compositional heterogeneity present in a common  beads-on-a-string type of multivalent IDP system from single-particle images in electron micrographs. This promising approach may be adopted to many other intrinsically disordered systems that have evaded traditional ensemble methods of characterization.

category: biophysics


title: Disruption of the gut microbiota attenuates epithelial ovarian cancer sensitivity to cisplatin therapy

abstract:
Epithelial Ovarian Cancer (EOC) is the leading cause of gynecologic cancer death. Despite many patients achieving remission with first-line therapy, up to 80% of patients will recur and require additional treatment. Retrospective clinical analysis of OC patients indicates antibiotic use during chemotherapy treatment is associated with poor overall survival. We assessed whether antibiotic (ABX) therapy would impact growth of EOC and sensitivity to cisplatin in murine models. Immune competent or compromised mice were given control or ABX containing water (metronidazole, ampicillin, vancomycin, and neomycin) before being intraperitoneally injected with murine EOC cells. Stool was collected to confirm microbiome disruption and tumors were monitored, and cisplatin therapy was administered weekly until endpoint. EOC tumor-bearing mice demonstrate accelerated tumor growth and resistance to cisplatin therapy in ABX treated compared with nonABX treatment. Stool analysis indicated most gut microbial species were disrupted by ABX treatment except for ABX resistant bacteria. To test for role of the gut microbiome, cecal microbiome transplants (CMTs) of microbiota derived from ABX or nonABX treated mice were used to recolonize the microbiome of ABX treated mice. nonABX cecal microbiome was sufficient to ameliorate the chemoresistance and survival of ABX treated mice indicative of a gut derived tumor suppressor. Mechanistically, tumors from ABX treated compared to nonABX treated mice contained a high frequency of cancer stem cells that were augmented by cisplatin. These studies indicate an intact microbiome provides a gut derived tumor suppressor and maintains chemosensitivity that is disrupted by ABX treatment.

SignificancePlatinum resistance is associated with poor prognosis and reduced therapeutic options for ovarian cancer patients. We identifed a tumor suppressive role of the gut microbiome that is disrupted upon antibiotic therapy.

category: cancer biology


title: A physicochemical model of odor sampling

abstract:
We present a general physicochemical sampling model for olfaction, based on established pharmacological laws, in which arbitrary combinations of odorant ligands and receptors can be generated and their individual and collective effects on odor representations and olfactory performance measured. Individual odor ligands exhibit receptor-specific affinities and efficacies; that is, they may bind strongly or weakly to a given receptor, and can act as strong agonists, weak agonists, partial agonists, or antagonists. Ligands interacting with common receptors compete with one another for dwell time; these competitive interactions appropriately simulate the degeneracy that fundamentally defines the capacities and limitations of odorant sampling. The outcome of these competing ligand-receptor interactions yields a pattern of receptor activation levels, thereafter mapped to glomerular presynaptic activation levels based on the convergence of sensory neuron axons. The metric of greatest interest is the mean discrimination sensitivity, a measure of how effectively the olfactory system at this level is able to recognize a small change in the physicochemical quality of a stimulus.

This model presents several significant outcomes, both expected and surprising. First, adding additional receptors reliably improves the systems discrimination sensitivity. Second, in contrast, adding additional ligands to an odor scene initially can improve discrimination sensitivity, but eventually will reduce it as the number of ligands increases. Third, the presence of antagonistic ligand-receptor interactions produced clear benefits for sensory system performance, generating higher absolute discrimination sensitivities and increasing the numbers of competing ligands that could be present before discrimination sensitivity began to be impaired. Finally, the model correctly reflects and explains the modest reduction in odor discrimination sensitivity exhibited by transgenic mice in which the specificity of glomerular targeting by primary olfactory neurons is partially disrupted.

Author SummaryWe understand most sensory systems by comparing the responses of the system against objective external physical measurements. For example, we know that our ability to distinguish small changes in color is greater for some colors than for others, and that we can distinguish sounds more acutely when they are within the range of pitches used for speech. Similar principles presumably apply to the sense of smell, but odorous chemicals are harder to physically quantify than light or sound because they cannot be organized in terms of a straightforward physical variable like wavelength or frequency. That said, the physical properties of interactions between chemicals and cellular receptors (such as those in the olfactory system) are well understood. What we lack is a systematic framework within which these pharmacological principles can be organized to study odor sampling in the way that we have long studied visual and auditory sampling. We here propose and describe such a framework for odor sampling, and show that it successfully replicates some established but unexplained experimental results.


title: The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

abstract:
Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage).

Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r{approx}0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-{beta} frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship.

This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.


title: Spatio-temporal heterogeneity in hippocampal metabolism in control and epilepsy conditions.

abstract:
The hippocampuss dorsal and ventral parts are involved in different operative circuits, which functions vary in time during the night and day cycle. These functions are altered in epilepsy. Since energy production is tailored to function, we hypothesized that energy production would be space- and time-dependent in the hippocampus and that such organizing principle would be modified in epilepsy. Using metabolic imaging and metabolite sensing ex vivo, we show that the ventral hippocampus favors aerobic glycolysis over oxidative phosphorylation as compared to the dorsal part in the morning in control mice. In the afternoon, aerobic glycolysis is decreased, and oxidative phosphorylation increased. In the dorsal hippocampus, the metabolic activity varies less between these two times but is weaker than in the ventral. Thus, the energy metabolism is different along the dorsoventral axis and changes as a function of time in control mice. In an experimental model of epilepsy, we find a large alteration of such spatio-temporal organization. In addition to a general hypometabolic state, the dorsoventral difference disappears in the morning, when seizure probability is low. In the afternoon, when seizure probability is high, the aerobic glycolysis is enhanced in both parts, the increase being stronger in the ventral area. We suggest that energy metabolism is tailored to the functions performed by brain networks, which vary over time. In pathological conditions, the alterations of these general rules may contribute to network dysfunctions.

SIGNIFICANCE STATEMENTThe dorsal part of the hippocampus is involved in spatial learning and memory processes, while the ventral is implicated in motivational and emotional behavior. These functions change during the night and day cycle, and they are altered in epilepsy. Here we show that energy production (glycolysis versus oxidative phosphorylation) varies along the dorsoventral axis in a circadian manner ex vivo in control mice. These rules are altered in experimental epilepsy. Thus, energy production may be tailored to the function performed by hippocampal subdivisions and to the time of the day. Alterations in epilepsy may contribute to seizure generation and cognitive deficits.


title: A COVID Moonshot: assessment of ligand binding to the SARS-CoV-2 main protease by saturation transfer difference NMR spectroscopy.

abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological cause of the coronavirus disease 2019, for which no effective therapeutics are available. The SARS-CoV-2 main protease (Mpro) is essential for viral replication and constitutes a promising therapeutic target. Many efforts aimed at deriving effective Mpro inhibitors are currently underway, including an international open-science discovery project, codenamed COVID Moonshot. As part of COVID Moonshot, we used saturation transfer difference nuclear magnetic resonance (STD-NMR) spectroscopy to assess the binding of putative Mpro ligands to the viral protease, including molecules identified by crystallographic fragment screening and novel compounds designed as Mpro inhibitors. In this manner, we aimed to complement enzymatic activity assays of Mpro performed by other groups with information on ligand affinity. We have made the Mpro STD-NMR data publicly available. Here, we provide detailed information on the NMR protocols used and challenges faced, thereby placing these data into context. Our goal is to assist the interpretation of Mpro STD-NMR data, thereby accelerating ongoing drug design efforts.

category: biochemistry


title: Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

abstract: The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology1-10. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. We therefore conducted a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactome of both viruses, as well as their influence on transcriptome, proteome, ubiquitinome and phosphoproteome in a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon SARS-CoV-2 and SARS-CoV infections at different layers and identified unique and common molecular mechanisms of these closely related coronaviruses. The TGF-{beta} pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org) highlights many hotspots that can be targeted by existing drugs and it can guide rational design of virus- and host-directed therapies, which we exemplify by identifying kinase and MMPs inhibitors with potent antiviral effects against SARS-CoV-2.

category: systems biology


title: Characterisation of the role and regulation of Ultrabithorax in sculpting fine-scale leg morphology

abstract: Hox genes are expressed during embryogenesis and determine the regional identity of animal bodies along the antero-posterior axis. However, they also function post-embryonically to sculpt fine-scale morphology. To better understand how Hox genes are integrated into post-embryonic gene regulatory networks, we further analysed the role and regulation of Ultrabithorax (Ubx) during mesothoracic (T2) leg development in Drosophila melanogaster. Ubx represses leg trichomes in the proximal posterior region of the T2 femur (the so-called naked valley) and we found that it likely does so through activating the expression of microRNA-92a. We also identified a T2 leg enhancer of Ubx that recapitulates the temporal and regional activity of this Hox gene in these appendages. Analysis of motifs in this enhancer predicted that it is bound by Distal-less (Dll) and we found that knockdown of Dll results in the loss of trichomes on the T2 femur. This suggests that while Ubx activates microRNA-92a to repress trichomes in the naked valley region of the proximal femur, Dll may repress Ubx more distally to enable formation of trichomes. Taken together our results provide insights into how Ubx is integrated into a postembryonic gene regulatory network to determine fine-scale leg morphology.


title: Single cell transcriptomic profiling identifies molecular phenotypes of newborn human lung cells

abstract:
RationaleWhile animal model studies have extensively defined mechanisms controlling cell diversity in the developing mammalian lung, the limited data available from late stage human lung development represents a significant knowledge gap. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung.

MethodsSingle cell RNA sequencing generated transcriptional profiles of 5500 cells obtained from two one-day old human lungs (born at gestational ages of 39 and 31 weeks) from the LungMAP Human Tissue Core Biorepository at the University of Rochester. Frozen single cell isolates were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Single cell sequence data from 32000 postnatal day 1, 3, 7 and 10 mouse lung (n = 2 at each time point) cells generated by the LungMAP Research Center at Cincinnati Childrens Hospital and Medical Center, using Dropseq platform, was integrated with the human data. In situ hybridization was used to confirm the spatial location of cellular phenotypes.

ResultsTranscriptional interrogation of donor newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, and immune cells and signature genes for each of these populations were identified. Computational integration of newborn human and postnatal mouse lung development cellular transcriptomes facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the human and mouse cellular transcriptomes also demonstrated cell type-specific differences in developmental states of the newborn human lung cells. In particular, matrix fibroblasts could be separated into those representative of younger cells (n=393), or older cells (n=158).

This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of development. Our results indicate that integrated single cell RNA profiling of human and mouse lung will help identify common and species-specific mechanisms of lung development and respiratory disease.

category: genomics


title: Gut microbial taxa elevated by dietary sugar disrupt memory function

abstract: Emerging evidence highlights a critical relationship between gut microbiota and neurocognitive development. Excessive consumption of sugar and other unhealthy dietary factors during early life developmental periods yields changes in the gut microbiome as well as neurocognitive impairments. However, it is unclear whether these two outcomes are functionally connected. Here we explore whether excessive early life consumption of added sugars negatively impacts memory function via the gut microbiome. Rats were given free access to a sugar-sweetened beverage (SSB) during the adolescent stage of development. Memory function and anxiety-like behavior were assessed during adulthood and gut bacterial and brain transcriptome analyses were conducted. Taxa-specific microbial enrichment experiments examined the functional relationship between sugar-induced microbiome changes and neurocognitive and brain transcriptome outcomes. Chronic early life sugar consumption impaired adult hippocampal-dependent memory function without affecting body weight or anxiety-like behavior. Adolescent SSB consumption during adolescence also altered the gut microbiome, including elevated abundance of two species in the genus Parabacteroides (P. distasonis and P. johnsonii) that were negatively correlated with hippocampal function. Transferred enrichment of these specific bacterial taxa in adolescent rats impaired hippocampal-dependent memory during adulthood. Hippocampus transcriptome analyses revealed that early life sugar consumption altered gene expression in intracellular kinase and synaptic neurotransmitter signaling pathways, whereas Parabacteroides microbial enrichment altered gene expression in pathways associated with metabolic function, neurodegenerative disease, and dopaminergic signaling. Collectively these results identify a role for microbiota "dysbiosis" in mediating the detrimental effects of early life unhealthy dietary factors on hippocampal-dependent memory function.


title: Chloroplast acquisition without the gene transfer in kleptoplastic sea slugs, Plakobranchus ocellatus

abstract: Some sea slugs sequester chloroplasts from algal food in their intestinal cells and photosynthesize for months. This phenomenon, kleptoplasty, poses a question of how the chloroplast retains its activity without the algal nucleus. There have been debates on the horizontal transfer of algal genes to the animal nucleus. To settle the arguments, this study reported the genome of a kleptoplastic sea slug Plakobranchus ocellatus and found no evidence of photosynthetic genes encoded on the nucleus. Nevertheless, it was confirmed that light illumination prolongs the life of mollusk under starvation. These data presented a paradigm that a complex adaptive trait, as typified by photosynthesis, can be transferred between eukaryotic kingdoms by a unique organelle transmission without nuclear gene transfer. Our phylogenomic analysis showed that genes for proteolysis and immunity undergo gene expansion and are up-regulated in chloroplast-enriched tissue, suggesting that these molluskan genes are involved in the DNA-independent phenotype acquisition.


title: Representation Learning of Resting State fMRI with Variational Autoencoder

abstract: Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Latent representations are clustered by both individuals and brain states. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available per subjects.


title: Modulation of inhibitory communication coordinates looking and reaching

abstract: Looking and reaching are controlled by different brain regions and coordinated during natural behaviour1-3. Understanding how flexible, natural behaviours like coordinated looking-and-reaching are controlled depends on understanding how neurons in different regions of the brain communicate4. Excitatory multiregional communication recruits neural coherence in a gamma-frequency (40-90 Hz) band5. Inhibitory control mechanisms are also required to flexibly control behaviour6, but little is known about how neurons in one region transiently suppress individual neurons in another to support behaviour. How does neuronal firing in a sender-region transiently suppress firing in a receiver-region? Here, we study inhibitory communication during a flexible, natural behaviour, termed gaze-anchoring, in which saccades are transiently inhibited by coordinated reaches. During gaze-anchoring, we find that neurons in the reach region of the posterior parietal cortex can inhibit neuronal firing in the parietal saccade region to suppress eye movements and improve reach accuracy. Importantly, suppression is transient, only present around the coordinated reach, and greatest when reach neurons fire spikes at a particular phase of beta-frequency (15-25 Hz) activity, not gamma-frequency activity. Our work provides evidence in the activity of single neurons for a novel mechanism of inhibitory communication in which beta-frequency neural coherence transiently inhibits multiregional communication to flexibly coordinate our natural behaviour.


title: Optimisation of functional network resources when learning behavioural strategies for performing complex tasks

abstract:
We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This  fine-tuning of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands.

HighlightsO_LIWe examine the optimisation of behaviour and brain-network resources during a novel "self-ordered switching" (SOS) paradigm.
C_LIO_LITask performance depended on generating behavioural routines with low algorithmic complexity (i.e., structured behaviours).
C_LIO_LIBehaviour became more structured and reaction time decreased as SOS was practised.
C_LIO_LIAs behaviour became more structured, activation in multiple-demand regions decreased for simple trial events but increased for executive switching events
C_LIO_LIIncreases in between-network functional connectivity correlate with reaction time decreases.
C_LI


title: Cryo-EM structure of the SARS-CoV-2 3a ion channel in lipid nanodiscs

abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes the coronavirus disease 2019 (COVID-19). SARS-CoV-2 encodes three putative ion channels: E, 8a, and 3a1,2. 3a is expressed in SARS patient tissue and anti-3a antibodies are observed in patient plasma3-6. 3a has been implicated in viral release7, inhibition of autophagy8, inflammasome activation9, and cell death10,11 and its deletion reduces viral titer and morbidity in mice1, raising the possibility that 3a could be an effective vaccine or therapeutic target3,12. Here, we present the first cryo-EM structures of SARS-CoV-2 3a to 2.1 [A] resolution and demonstrate 3a forms an ion channel in reconstituted liposomes. The structures in lipid nanodiscs reveal 3a dimers and tetramers adopt a novel fold with a large polar cavity that spans halfway across the membrane and is accessible to the cytosol and the surrounding bilayer through separate water- and lipid-filled openings. Electrophysiology and fluorescent ion imaging experiments show 3a forms Ca2+-permeable non-selective cation channels. We identify point mutations that alter ion permeability and discover polycationic inhibitors of 3a channel activity. We find 3a-like proteins in multiple Alphacoronavirus and Betacoronavirus lineages that infect bats and humans. These data show 3a forms a functional ion channel that may promote COVID-19 pathogenesis and suggest targeting 3a could broadly treat coronavirus diseases.


title: ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer

abstract:
Immunotherapies provide effective treatments for previously untreatable tumors and identifying tumor-specific epitopes can help elucidate the molecular determinants of therapy response. Here, we describe a pipeline, ISOTOPE (ISOform-guided prediction of epiTOPEs In Cancer), for the comprehensive identification of tumor-specific splicing-derived epitopes. Using RNA sequencing and mass spectrometry for MHC-I associated proteins, ISOTOPE identified neoepitopes from tumor-specific splicing events that are potentially presented by MHC-I complexes. Analysis of multiple samples indicates that splicing alterations may affect the production of self-epitopes and generate more candidate neoepitopes than somatic mutations. Although there was no difference in the number of splicing-derived neoepitopes between responders and non-responders to immune therapy, higher MHC-I binding affinity was associated with a positive response. Our analyses highlight the diversity of the immunogenic impacts of tumor-specific splicing alterations and the importance of studying splicing alterations to fully characterize tumors in the context of immunotherapies. ISOTOPE is available at https://github.com/comprna/ISOTOPE

Author summaryImmune cells have the ability to attack tumor cells upon the identification of tumor-specific peptides, i.e., epitopes, that are presented by the major histocompatibility complex (MHC). New cancer immunotherapies that help trigger this process provide a promising therapeutic strategy. One crucial aspect for their success is the ability to determine the molecular properties of a tumor that are informative about the effectiveness of the therapy. Alterations in the way genes are processed to express RNA molecules could lead to the production of new peptides, with some of them potentially being presented as tumor epitopes and facilitate the attack of immune cells. It is therefore essential to facilitate the identification of these splicing-derived epitopes. In this work, we describe a computational pipeline that performs a comprehensive identification of splicing alterations in a tumor and the potential epitopes that they would produce. Analysis of tumor samples with our pipeline show that responders and non-responders to immune therapy do not show differences in the number of splicing-derived epitopes, but splicing neoepitopes have higher affinity to the MHC complex in responders. Our new pipeline facilitates the genome-scale analysis of the role of splicing alterations in shaping the molecular properties that influence response to immunotherapy.


title: Analysis of overlapping genetic association in type 1 and type 2 diabetes

abstract:
Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently.

MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [≥]0.9 was considered a genuine shared association with both diseases.

ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [≥]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect.

Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases.

Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes.
C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell.
C_LI

What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases?
C_LI

What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases.
C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect.
C_LI

How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward.
C_LI

category: genetics


title: A multi-enhancer RET regulatory code is disrupted in Hirschsprung disease

abstract: The major genetic risk factors for Hirschsprung disease (HSCR) are three common polymorphisms within cis regulatory elements (CREs) of the RET receptor tyrosine kinase gene that reduce its gene expression during enteric nervous system (ENS) development. These variants have synergistic effects on RET gene expression and additionally dysregulate other ENS and HSCR genes in the RET-EDNRB gene regulatory network (GRN). Here, we use siRNA, ChIP and CRISPR/Cas9 deletion analyses in the SK-N-SH cell line to ask, how many HSCR-associated risk variants reside in CREs and affect RET gene expression? We demonstrate that 31 HSCR-associated variants reside in candidate RET CREs, ten with differential allele-specific in vitro enhancer activity and seven affecting RET gene expression; of these, five bind the transcription factors PAX3, RARB, GATA2 and SOX10. These and our prior results demonstrate that common sequence variants in at least 10 RET enhancers affect HSCR risk, extending the known RET-EDNRB GRN to reveal an extensive regulatory code modulating disease risk even at a single gene.


title: Computational Model of G2-M DNA Damage Checkpoint Regulation in Normal and p53-null Cancer Cells

abstract: The authors have withdrawn their manuscript due to authorship disputes. Therefore, they do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.


title: 3D-printed drug testing platform based on a 3D model of aged human skeletal muscle

abstract: Three-dimensional engineering of skeletal muscle is becoming increasingly relevant for tissue engineering, disease modeling and bio-hybrid robotics, where flexible, versatile and multidisciplinary approaches for the evaluation of tissue differentiation, functionality and force measurement are required. This works presents a 3D-printed platform of bioengineered human skeletal muscle which can efficiently model the three-dimensional structure of native tissue, while providing information about force generation and contraction profiles. Proper differentiation and maturation of myocytes is demonstrated by the expression of key myo-proteins using immunocytochemistry and analyzed by confocal microscopy, and the functionality assessed via electrical stimulation and analysis of contraction kinetics. To validate the flexibility of this platform for complex tissue modelling, the bioengineered muscle is treated with tumor necrosis factor  to mimic the conditions of aging, which is supported by morphological and functional changes. Moreover, as a proof of concept, the effects of Argireline(R) Amplified peptide, a cosmetic ingredient that causes muscle relaxation, are evaluated in both healthy and aged tissue models. Therefore, the results demonstrate that this 3D-bioengineered human muscle platform could be used to assess morphological and functional changes in the aging process of muscular tissue with potential applications in biomedicine, cosmetics and bio-hybrid robotics.


title: The validation of new phase-dependent gait stability measures: a modelling approach

abstract: Identification of individuals at risk of falling is important when designing fall prevention methods. Current measures that estimate gait stability and robustness appear limited in predicting falls in older adults. Inspired by recent findings on changes in phase-dependent local stability within a gait cycle, we devised several phase-dependent stability measures and tested for their usefulness to predict gait robustness in compass walker models. These measures are closely related to the often-employed maximum finite-time Lyapunov exponent and maximum Floquet multiplier that both assess a systems response to infinitesimal perturbations. As such, they entail linearizing the system but this is realized in a rotating hypersurface orthogonal to the period-one solution followed by estimating the trajectory-normal divergence rate of the swing phases and the foot strikes. We correlated the measures with gait robustness, i.e. the largest perturbation a walker can handle, in two compass walker models with either point or circular feet to estimate their prediction accuracy. To also test for the dependence of the measures under state space transform, we represented the point-feet walker in both Euler-Lagrange and Hamiltonian canonical form. Our simulations revealed that for most of the measures their correlation with gait robustness differs between models and between different state space forms. In particular the latter may jeopardize many stability measures predictive capacity for gait robustness. The only exception that consistently displayed strong correlations is the divergence of foot strike. Our results admit challenges of using phase-dependent stability measures as objective means to estimate the risk of falling.


title: The novel, recurrent mutation in the TOP2A gene results in the enhanced topoisomerase activity and transcription deregulation in glioblastoma

abstract:
BackgroundHigh grade gliomas (HGGs) are aggressive, primary brain tumors with poor clinical outcomes. We aim to better understand glioma pathobiology and find potential therapeutic susceptibilities.

MethodsWe designed a custom panel of 664 cancer- and epigenetics-related genes, and employed targeted next generation sequencing to study the genomic landscape of somatic and germline variants in 182 gliomas of different malignancy grades. mRNA sequencing was performed to detect transcriptomic abnormalities.

ResultsIn addition to known alterations in TP53, IDH1, ATRX, EGFR genes found in this cohort, we identified a novel, recurrent mutation in the TOP2A gene coding for Topoisomerase 2A occurring only in glioblastomas (GBM, WHO grade IV gliomas). Biochemical assays with recombinant proteins demonstrated stronger DNA binding and DNA supercoil relaxation activities of the variant proteins. GBM patients carrying the mutated TOP2A had shorter overall survival than those with the wild type TOP2A. Computational analyses of transcriptomic data showed that GBMs with the mutated TOP2A have different transcriptomic patterns suggesting higher transcriptomic activity.

ConclusionWe identified a novel TOP2A E948Q variant that strongly binds to DNA and is more active than the wild type protein. Our findings suggest that the discovered TOP2A variant is gain-of-function mutation.

Key pointsO_LIThe most frequent genetic alterations in high grade gliomas are reported.
C_LIO_LIA new mutation in the TOP2A gene was found in 4 patients from Polish population.
C_LIO_LIA E948Q substitution changes TOP2A activities towards DNA.
C_LIO_LIThe recurrent TOP2A variant is a gain-of-function mutation.
C_LI

Importance of the studyGlioblastoma is a deadly disease. Despite recent advancements in genomics and innovative targeted therapies, glioblastoma therapy has not shown improvements. Insights into glioblastoma biology may improve diagnosis, prognosis, and treatment prediction, directing to a better outcome. We performed targeted sequencing of 664 cancer genes, and identified a new variant of the TOP2A gene encoding topoisomerase 2A in glioblastomas. The TOP2A protein variant shows a higher affinity towards DNA and causes transcriptional alterations, suggesting a higher de novo transcription rate.


title: Virulence strategies of an insect herbivore and oomycete plant pathogen converge on a host E3 SUMO ligase

abstract: Pathogens and pests secrete proteins (effectors) to interfere with plant immunity through modification of host target functions and disruption of immune signalling networks. Importantly, molecular virulence strategies of distinct pathogens converge on a small set of regulators with central roles in plant immunity. The extent of convergence between pathogen and herbivorous insect virulence strategies is largely unexplored. We found that effectors from the oomycete pathogen, Phytophthora capsici, and the major aphid pest, Myzus persicae target the host immune regulator SIZ1, an E3 SUMO ligase. We show that the oomycete and aphid effector, which both contribute to virulence, feature different activities towards SIZ1. While M. persicae effector Mp64 increases SIZ1 protein levels, P. capsici effector CRN83_152 enhances SIZ1-E3 SUMO ligase activity in vivo. Loss of SIZ1 in host plants leads to reduced host susceptibility to aphids and an oomycete pathogen. Our results suggest convergence of distinct pathogen and pest virulence strategies on an E3 SUMO ligase to enhance host susceptibility.

category: plant biology


title: Patterns of recent natural selection on genetic loci associated with sexually differentiated human body size and shape phenotypes

abstract:
Levels of sex differences for human body size and shape phenotypes are hypothesized to have adaptively reduced following the agricultural transition as part of an evolutionary response to relatively more equal divisions of labor and new technology adoption. In this study, we tested this hypothesis by studying genetic variants associated with five sexually differentiated human phenotypes: height, body mass, hip circumference, body fat percentage, and waist circumference. We first analyzed genome-wide association (GWAS) results for UK Biobank individuals ([~]197,000 females and [~]167,000 males) to identify a total of 119,023 single nucleotide polymorphisms (SNPs) significantly associated with at least one of the studied phenotypes in females, males, or both sexes (P<5x10-8). From these loci we then identified 3,016 SNPs (2.5%) with significant differences in the strength of association between the female- and male-specific GWAS results at a low false-discovery rate (FDR<0.001). Genes with known roles in sexual differentiation are significantly enriched for co-localization with one or more of these SNPs versus SNPs associated with the phenotypes generally but not with sex differences (2.93-fold enrichment; permutation test; P=0.0041). We also confirmed that the identified variants are disproportionately associated with greater phenotype effect sizes in the sex with the stronger association value. We then used the singleton density score statistic, which quantifies recent (within the last [~]3,000 years; post-agriculture adoption in Britain) changes in the frequencies of alleles underlying polygenic traits, to identify a signature of recent positive selection on alleles associated with greater body fat percentage in females (permutation test; P=0.0038; FDR=0.0380), directionally opposite to that predicted by the sex differentiation reduction hypothesis. Otherwise, we found no evidence of positive selection for sex difference-associated alleles for any other trait. Overall, our results challenge the longstanding hypothesis that sex differences adaptively decreased following subsistence transitions from hunting and gathering to agriculture.

Author SummaryThere is uncertainty regarding the evolutionary history of human sex differences for quantitative body size and shape phenotypes. In this study we identified thousands of genetic loci that differentially impact body size and shape trait variation between females and males using a large sample of UK Biobank individuals. After confirming the biological plausibility of these loci, we used a population genomics approach to study the recent (over the past 3,000 years) evolutionary histories of these loci in this population. We observed significant increases in the frequencies of alleles associated with greater body fat percentage in females. This result is contradictory to longstanding hypotheses that sex differences have adaptively decreased following subsistence transitions from hunting and gathering to agriculture.


title: Constitutive activation of the PI3K-Akt-mTORC1 pathway sustains the m.3243A>G mtDNA mutation

abstract: Mutations of the mitochondrial genome (mtDNA) cause a range of profoundly debilitating clinical conditions for which treatment options are very limited. Most mtDNA diseases show heteroplasmy - tissues express both wild-type and mutant mtDNA. While the level of heteroplasmy broadly correlates with disease severity, the relationships between specific mtDNA mutations, heteroplasmy, disease phenotype and severity are poorly understood. We have carried out extensive bioenergetic, metabolomic and RNAseq studies on heteroplasmic patient derived cells carrying the most prevalent disease related mtDNA mutation, m.3243A>G. These studies reveal that the mutation promotes changes in metabolites which is associated with the upregulation of the PI3K-Akt-mTORC1 axis in patient-derived cells and tissues. Remarkably, pharmacological inhibition of PI3K, Akt, or mTORC1 activated mitophagy, reduced mtDNA mutant load and rescued cellular bioenergetic function. The rescue was prevented by inhibition of mitophagy. The PI3K-Akt-mTORC1 axis thus represents a potential therapeutic target that may benefit people suffering from the consequences of the m.3243A>G mutation.


title: Temporal dynamics of the neural representation of hue and luminance contrast

abstract: Hue and luminance contrast are basic visual features, yet the timing of the neural computations that extract them, and whether they depend on common neural circuits, is not well established. Using multivariate analyses of magnetoencephalography data, we show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not appear to depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data; the pattern of results suggests that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.


title: Temporal dynamics of the neural representation of hue and luminance polarity


title: Neuronal variability reflects probabilistic inference tuned to natural image statistics

abstract: Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike-count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses -- their variability -- can be explained by a probabilistic representation tuned to naturalistic inputs.


title: Two opposite voltage-dependent currents control the unusual early development pattern of embryonic Renshaw cell electrical activity

abstract: Renshaw cells (V1R) are excitable as soon as they reach their final location next to the spinal motoneurons and are functionally heterogeneous. Using multiple experimental approaches, in combination with biophysical modeling and dynamical systems theory, we analyzed, for the first time, the mechanisms underlying the electrophysiological properties of V1R during early embryonic development of the spinal cord locomotor networks (E11.5-E16.5). We found that these interneurons are subdivided into several functional clusters from E11.5 and then display an unexpected transitory involution process during which they lose their ability to sustain tonic firing. We demonstrated that the essential factor controlling the diversity of the discharge pattern of embryonic V1R is the ratio of a persistent sodium conductance to a delayed rectifier potassium conductance. Taken together, our results reveal how a simple mechanism, based on the synergy of two voltage-dependent conductances that are ubiquitous in neurons, can produce functional diversity in V1R and control their early developmental trajectory.


title: Outdoor Atmospheric Microbial Diversity is Associated with Urban Landscape Structure and Differs from Indoor-Transit Systems as Revealed by Mobile Monitoring and Three-Dimensional Spatial Analysis

abstract:
Microbes are abundant inhabitants of the near-surface atmosphere in urban areas. The distribution of microbial communities may benefit or hinder human wellbeing and ecosystem function. Surveys of airborne microbial diversity are uncommon in both natural and built environments and those that investigate diversity are stationary in the city, thus missing continuous exposure to microbes that covary with three-dimensional urban structure. Individuals in cities are generally mobile and would be exposed to diverse urban structures outdoors and within indoor-transit systems in a day. We used mobile monitoring of microbial diversity and geographic information system spatial analysis, across Philadelphia, Pennsylvania, USA in outdoor and indoor-transit (subways and train cars) environments. This study identifies to the role of the three-dimensional urban landscape in structuring atmospheric microbiomes and employs mobile monitoring over ~1920 kilometers to measure continuous biodiversity. We found more diverse communities outdoors that significantly differ from indoor-transit air in microbial community structure, function, likely source environment, and potentially pathogenic fraction of the community. Variation in the structure of the urban landscape was associated with diversity and function of the near-surface atmospheric microbiome in outdoor samples.

ImportanceGlobal nutrient cycling and human health depend on the rich biodiversity of microorganisms. The influence of the urban environment on microbiomes remains poorly described despite cities being the fastest growing ecosystems. All life is exposed to the atmosphere and thus discerning what microbes are present and what their functions may be is critical to create resistant and resilient cities under climate change. This study combines a spatially explicit analysis of urban structure with mobile monitoring of the atmospheric microbiome.

category: ecology


title: Coupled Dynamics of Behavior and Disease Contagion Among Antagonistic Groups

abstract: Disease transmission and behavior change are both fundamentally social phenomena. Behavior change can have profound consequences for disease transmission, and epidemic conditions can favor the more rapid adoption of behavioral innovations. We analyze a simple model of coupled behavior-change and infection in a structured population characterized by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioral divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behavior-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a "second wave" in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarized populations such as the United States.


title: Short sequence motif dynamics in the SARS-CoV-2 genome suggest a role for cytosine deamination in CpG reduction.

abstract: RNA viruses use CpG reduction to evade the host cell defense, but the driving mechanisms are still largely unknown. In an attempt to address this we used a rapidly growing genomic dataset of SARS-CoV-2 with relevant metadata information. Remarkably, by simply ordering SARS-CoV-2 genomes by their date of collection, we find a progressive increase of C-to-U substitutions resulting in 5'-UCG-3' motif reduction that in turn have reduced the CpG frequency over just a few months of observation. This is consistent with APOBEC-mediated RNA editing resulting in CpG reduction, thus allowing the virus to escape ZAP-mediated RNA degradation. Our results thus link the dynamics of target sequences in the viral genome for two known host molecular defense mechanisms, mediated by the APOBEC and ZAP proteins.


title: Disruption of the MreB elongasome is overcome by mutations in the TCA cycle

abstract: The bacterial actin homolog, MreB, is highly conserved among rod-shaped bacteria and essential for growth under normal growth conditions. MreB directs the localization of cell wall synthesis and loss of MreB results in round cells and death. Using the MreB depolymerizing drug, A22, we show that changes to central metabolism through deletion of malate dehydrogenase from the TCA cycle results in cells with an increased tolerance to A22. We hypothesize that deletion of malate dehydrogenase leads to the upregulation of gluconeogenesis resulting in an increase in cell wall precursors. Consistent with this idea, metabolite analysis revealed that mdh deletion cells possess elevated levels of several glycolysis/gluconeogenesis compounds and the cell wall precursor, UDP-NAG. In agreement with these results, the increased A22 resistance phenotype can be recapitulated through the addition of glucose to the media. Finally, we show that this increase in antibiotic tolerance is not specific to A22 but also applies to the cell wall-targeting antibiotic, mecillinam.


title: Modelling the effects of lymph node swelling on T-cell response

abstract: Swelling of lymph nodes is commonly observed during the adaptive immune response, yet the impact on T cell trafficking and subsequent immune response is not well known. To better understand the effect of macro-scale alterations, we developed an agent-based model of the lymph node para-cortex, describing T cell trafficking and response to antigen-presenting dendritic cells alongside swelling-induced changes in T cell recruitment and egress, and regulation of expression of egress-modulating T cell receptor Sphingosine-1-phosphate receptor-1. Analysis of effector T cell response under varying swelling conditions showed that swelling consistently aided T cell activation. However, subsequent effector CD8+ T cell production could be reduced if swelling occurred too early in the T cell proliferative phase or if T cell cognate frequency was low due to increased opportunity for T cell exit. Temporarily extending retention of newly differentiated effector T cells, mediated by Sphingosine-1-phosphate receptor-1 expression, mitigated any negative effects of swelling by allowing facilitation of activation to outweigh increased access to exit areas. These results suggest targeting temporary effector T cell retention and egress associated with swelling may offer new ways to modulate effector TC responses in, for example, immuno-suppressed patients and optimisation of vaccine design.

category: immunology


title: Lymph node swelling combined with temporary effector T cell retention aids T cell response in a model of adaptive immunity


title: Sailing in rough waters: examining volatility of fMRI noise

abstract:
BackgroundThe assumption that functional magnetic resonance imaging (fMRI) noise has constant volatility has recently been challenged by studies examining heteroscedasticity arising from head motion and physiological noise. The present study builds on this work using latest methods from the field of financial mathematics to model fMRI noise volatility.

MethodsMulti-echo nd human fMRI scans were used and realised volatility was estimated. The Hurst parameter H [isin] (0, 1), which governs the roughness/irregularity of realised volatility time series, was estimated. Calibration of H was performed pathwise, using well-established neural network calibration tools.

ResultsIn all experiments the volatility calibrated to values within the rough case, H < 0.5, and on average fMRI noise was very rough with 0.03 < H < 0.05. Some edge effects were also observed, whereby H was larger near the edges of the phantoms.

DiscussionThe findings suggest that fMRI volatility is not only non-constant, but also substantially more irregular than a standard Brownian motion. Thus, further research is needed to examine the impact such pronounced oscillations in the volatility of fMRI noise have on data analyses.


title: A system for phenotype harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

abstract: Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 TOPMed studies per phenotype. We discuss the challenges faced in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.


title: Live-cell single-molecule tracking highlights requirements for stable Smc5/6 chromatin association in vivo

abstract: The essential Smc5/6 complex is required in response to replication stress and is best known for ensuring the fidelity of homologous recombination. Using single-molecule tracking in live fission yeast to investigate Smc5/6 chromatin association, we show that Smc5/6 is chromatin associated in unchallenged cells and this depends on the non-SMC protein Nse6. We define a minimum of two Nse6-dependent sub-pathways, one of which requires the BRCT-domain protein Brc1. Using defined mutants in genes encoding the core Smc5/6 complex subunits we show that the Nse3 double-stranded DNA binding activity and the arginine fingers of the two Smc5/6 ATPase binding sites are critical for chromatin association. Interestingly, disrupting the ssDNA binding activity at the hinge region does not prevent chromatin association but leads to elevated levels of gross chromosomal rearrangements during replication restart. This is consistent with a downstream function for ssDNA binding in regulating homologous recombination.

category: molecular biology


title: Brainprints: identifying individuals from magnetoencephalograms

abstract:
Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing, the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/159913v3_fig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@50c58corg.highwire.dtl.DTLVardef@d99cc1org.highwire.dtl.DTLVardef@c255cdorg.highwire.dtl.DTLVardef@8f2c13_HPS_FORMAT_FIGEXP  M_FIG O_FLOATNOFigure 1:C_FLOATNO Graphical abstract.

Identifying which subject a segment of MEG data belongs to is strikingly easy when other data from the same session is available for every subject. We propose three types of interpretable features that can also be used to identify individuals across sessions with high accuracy. Identifiability of individuals is influenced by factors such as resting state vs. task state, components of each feature, the sample size and the level of preprocessing. Our results reveal aspects of individual variability in MEG signals and highlight privacy risks associated with MEG data sharing.

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title: Can expectation suppression be explained by reduced attention to predictable stimuli?

abstract: The expectation-suppression effect - reduced stimulus-evoked responses to expected stimuli - is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that that expectation suppression could be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. We base our discussion mainly on findings in the visual cortex and propose that resolving this controversy requires the assessment of qualitative differences between the ways in which attention and  surprise enhance brain responses.


title: GenNet framework: interpretable neural networks for phenotype prediction

abstract:
Deep learning is rarely used in population genomics because of the computational burden and challenges in interpreting neural networks. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biological knowledge from public databases, resulting in neural networks that contain only biological plausible connections.

We applied the framework to seventeen phenotypes from a case-control study, a population-based study and the UK Biobank. Interpreting the networks revealed well-replicated genes such as HERC2 and OCA2 for hair and eye color and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework obtained an AUC of 0.74 in the held-out test set and identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia.

GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.


title: Dengue virus clinical isolates sustain viability of infected hepatic cells by counteracting apoptosis-mediated DNA breakage

abstract: NS1, a virotoxin, abundantly present in Dengue patients blood, is a major player behind disease patho-biogenesis including plasma leakage and damage to the liver. Despite the presence of NS1 in blood, Dengue is asymptomatic and self-limiting in [≥]80% Dengue virus (DV) infected people. We investigated this observation and found that plasmid-mediated NS1 expression and secretion in liver cells (Huh7) are sufficient to cause programmed cell death (apoptosis) and associated cellular DNA breakage. However, liver or kidney cell lines infected with DV and secreting equivalent amounts of NS1 didnt exhibit apoptotic DNA breakage. In fact, DV-infected cells showed better survival than cells in which only NS1 was transiently expressed by transfection. We also found that DV can even prevent chemical-induced apoptotic DNA damage in infected host cells. So, DV thwarts host antiviral defence i.e. apoptosis, by counteracting cellular DNA breakages and keeps the infected cells metabolically active to prolong virus replication.


title: Sequence structure context organizes items in varied latent states of working memory neural network

abstract: In memory experiences, events do not exist independently but are linked with each other via structure-based organization. Structure context largely influences memory behavior, but how it is implemented in the brain remains unknown. Here, we combined magnetoencephalogram (MEG) recordings, computational modeling, and impulse-response approaches to probe the latent states when subjects held a list of items in working memory (WM). We demonstrate that sequence context reorganizes WM items into distinct latent states, i.e., being reactivated at different latencies during WM retention, and the reactivation profiles further correlate with recency behavior. In contrast, memorizing the same list of items without sequence task requirements disrupts the recency effect and elicits comparable neural reactivations. Computational modeling further reveals a dominant function of sequence context, instead of passive memory decaying, in characterizing recency effect. Taken together, sequence structure context shapes the way WM items are stored in the human brain and essentially influences memory behavior.


title: Sequence structure organizes items in varied latent states of working memory neural network