bluuebunny/crossref_metadata_2025_split huggingface dataset:
Host-parasite coevolution promotes innovation through deformations in fitness landscapes
Authors: Gupta Animesh , Zaman Luis , Strobel Hannah M. , Gallie Jenna , Burmeister Alita R. , Kerr Benjamin , Tamar Einat S. , Kishony Roy , Meyer Justin R. 
Published: June 2021

AbstractDuring the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli. Through computer simulations of λ’s evolution on the empirical fitness landscape, we showed that λ was more likely to evolve to use a new receptor if it experienced a shift in its fitness landscape caused by coevolution. This result was further validated by additional laboratory experiments. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.
OSCAR: a framework to identify and quantify cells in densely packed three-dimensional biological samples
Authors: Ledesma-Terrón Mario , Pérez-Dones Diego , Míguez David G. 
Published: June 2021

AbstractWe have developed an Object Segmentation, Counter and Analysis Resource (OSCAR) that is designed specifically to quantify densely packed biological samples with reduced signal-to-background ratio. OSCAR uses as input three dimensional images reconstructed from confocal 2D sections stained with dies such as nuclear marker and immunofluorescence labeling against specific antibodies to distinguish the cell types of interest. Taking advantage of a combination of arithmetic, geometric and statistical algorithms, OSCAR is able to reconstruct the objects in the 3D space bypassing segmentation errors due to the typical reduced signal to noise ration of biological tissues imaged in toto. When applied to the zebrafish developing retina, OSCAR is able to locate and identify the fate of each nuclei as a cycling progenitor or a terminally differentiated cell, providing a quantitative characterization of the dynamics of the developing vertebrate retina in space and time with unprecedented accuracy.
Automated movement assessment in stroke rehabilitation
Authors: Ahmed Tamim , Thopalli Kowshik , Rikakis Thanassis , Turaga Pavan , Kelliher Aisling , Huang Jia-Bin , Wolf Steve 
Published: June 2021

ABSTRACTWe are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor’s movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).
Dopamine-independent state inference mediates expert reward guided decision making
Authors: Blanco-Pozo Marta , Akam Thomas , Walton Mark E. 
Published: June 2021

AbstractRewards are thought to influence future choices through dopaminergic reward prediction errors (RPEs) updating stored value estimates. However, accumulating evidence suggests that inference about hidden states of the environment may underlie much adaptive behaviour, and it is unclear how these two accounts of reward-guided decision-making should be integrated. Using a two-step task for mice, we show that dopamine reports RPEs using value information inferred from task structure knowledge, alongside information about recent reward rate and movement. Nonetheless, although rewards strongly influenced choices and dopamine, neither activating nor inhibiting dopamine neurons at trial outcome affected future choice. These data were recapitulated by a neural network model in which frontal cortex learned to track hidden task states by predicting observations, while basal ganglia learned corresponding values and actions via dopaminergic RPEs. Together, this two-process account reconciles how dopamine-independent state inference and dopamine-mediated reinforcement learning interact on different timescales to determine reward-guided choices.
Genetic basis and dual adaptive role of floral pigmentation in sunflowers
Authors: Todesco Marco , Bercovich Natalia , Kim Amy , Imerovski Ivana , Owens Gregory L. , Ruiz Óscar Dorado , Holalu Srinidhi V. , Madilao Lufiani L. , Jahani Mojtaba , Légaré Jean-Sébastien , Blackman Benjamin K. , Rieseberg Loren H. 
Published: June 2021

AbstractVariation in floral displays, both between and within species, has been long known to be shaped by the mutualistic interactions that plants establish with their pollinators. However, increasing evidence suggests that abiotic selection pressures influence floral diversity as well. Here we analyze the genetic and environmental factors that underlie patterns of floral pigmentation in wild sunflowers. While sunflower inflorescences appear invariably yellow to the human eye, they display extreme diversity for patterns of ultraviolet pigmentation, which are visible to most pollinators. We show that this diversity is largely controlled by cis-regulatory variation at a single MYB transcription factor, HaMYB111, through accumulation of UV-absorbing flavonol glycosides. As expected, different patterns of ultraviolet pigments in flowers have a strong effect on pollinator preferences. However, variation for floral ultraviolet patterns is also associated with environmental variables, especially relative humidity, across populations of wild sunflowers. Larger ultraviolet patterns, which are found in drier environments, limit transpiration, therefore reducing water loss. The dual role of floral UV patterns in pollination attraction and abiotic responses reveals the complex adaptive balance underlying the evolution of floral traits.
Evolutionary graph theory beyond pairwise interactions: higher-order network motifs shape times to fixation in structured populations
Authors: Kuo Yang Ping , Carja Oana 
Published: June 2021

AbstractTo design population topologies that can accelerate rates of solution discovery in directed evolution problems or in evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution under elevated mutation rate. Here we find that moving beyond dyadic interactions is fundamental to explain the trade-offs between probability and time to fixation. We show that higher-order motifs, and in particular three-node structures, allow tuning of times to fixation, without changes in probabilities of fixation. This gives a near-continuous control over achieving solutions that allow for a wide range of times to fixation. We apply our algorithms and analytic results to two evolutionary optimization problems and show that the rate at which evolving agents learn to navigate their environment can be tuned near continuously by adjusting the higher-order topology of the agent population. We show that the effects of population structure on the rate of evolution critically depend on the optimization landscape and find that decelerators, with longer times to fixation of new mutants, are able to reach the optimal solutions faster than accelerators in complex solution spaces. Our results highlight that no one population topology fits all optimization applications, and we provide analytic and computational tools that allow for the design of networks suitable for each specific task.
Risk modelling of single cell populations revealed the heterogeneity of immune infiltration in hepatocellular carcinoma
Authors: Wang Lu , Chen Yifan , Mao Fengbiao , Sun Zhongsheng , Liu Xiangdong 
Published: June 2021

ABSTRACTHepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies. E2F transcription factors play an important role in the tumorigenesis and progression of HCC, mainly through the RB/E2F pathway. Prognostic models for HCC based on gene signatures have been developed rapidly in recent years; however, their discriminating ability at the single cell level remains elusive, which could reflect the underlying mechanisms driving the sample bifurcation. In this study, we constructed and validated a predictive model based on E2F expression, successfully stratifying HCC patients into two groups with different survival risks. Then we used a single-cell dataset to test the discriminating ability of the predictive model on infiltrating T cells, demonstrating remarkable cellular heterogeneity as well as altered cell fates. We identified distinct cell subpopulations with diverse molecular characteristics. We also found that the distribution of cell subpopulations varied considerably across onset stages among patients, providing fundamental basis for patient-oriented precision evaluation. Moreover, single-sample gene set enrichment analysis revealed that subsets of CD8+ T cells with significantly different cell adhesion level could be associated with different patterns of tumor cell dissemination. Therefore, our findings linked the conventional prognostic gene signature to the immune microenvironment and cellular heterogeneity at the single-cell level, thereby providing deeper insights into the understanding of HCC tumorigenesis.
Negative curvature-promoting lipids instruct nuclear ingression of low autophagic potential vacuoles
Authors: Garcia Manon , Kumanski Sylvain , Elías-Villalobos Alberto , Soulet Caroline , Moriel-Carretero María 
Published: June 2021

AbstractMembrane contact sites are functional nodes at which organelles exchange information through moving ions, proteins and lipids, thus driving the reorganization of metabolic pathways and the adaptation to changing cues. The nuclear-vacuole junction of Saccharomyces cerevisiae is among the most extensive and better-known organelle contact sites, described to expand in response to various metabolic stresses. While using genotoxins with unrelated purposes, we serendipitously discovered a phenomenon that we describe as the most extreme and intimate contact ever reported between nuclei and vacuoles: the vacuole becomes completely internalized in the nucleus. We define lipids supporting negative curvature, such as phosphatidic acid and sterols, as bona-fide drivers of this event. Functionally, we purport that internalized vacuoles are low efficiency ones whose removal from the cytoplasm optimizes cargo interaction with functional vacuoles. Thus, our findings also point to nucleus-vacuole interactions as important for metabolic adaptation. Yet, rather than by inter-organelle exchanges, the underlying mechanism literally concurs with vacuolar sequestration.
Markovian Weighted Ensemble Milestoning (M-WEM): Long-time Kinetics from Short Trajectories
Authors: Ray Dhiman , Stone Sharon Emily , Andricioaei Ioan 
Published: June 2021

AbstractWe introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-timescale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond timescale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy, but it also allows for a scheme to reconstruct a multidimensional free energy landscape along additional degrees of freedom which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to four orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential application in kinetics and free-energy based computational drug design.
Characterization of an operon required for growth on cellobiose in Clostridioides difficile
Authors: Hasan Md Kamrul , Dhungel Babita Adhikari , Govind Revathi 
Published: June 2021

AbstractCellobiose metabolism is linked to the virulence properties in numerous bacterial pathogens. Here, we characterized a putative cellobiose PTS operon of Clostridiodes difficile to investigate the role of cellobiose metabolism in C. difficile pathogenesis. Our gene knockout experiments demonstrated that the putative cellobiose operon enables uptake of cellobiose into C. difficile and allows growth when cellobiose is provided as the sole carbon source in minimal medium. Additionally, using reporter gene fusion assays and DNA pull-down experiments, we show that its transcription is regulated by CelR, a novel transcriptional repressor protein, which directly binds to the upstream region of the cellobiose operon to control its expression. We have also identified cellobiose metabolism to play a significant role in C. difficile physiology as observed by the reduction of sporulation efficiency when cellobiose uptake was compromised in the mutant strain. In corroboration to in vitro study findings, our in vivo hamster challenge experiment showed a significant reduction of pathogenicity by the cellobiose mutant strain in both the primary and the recurrent infection model- substantiating the role of cellobiose metabolism in C. difficile pathogenesis.
Structure of the mini-RNA-guided endonuclease CRISPR-CasΦ3
Authors: Carabias Arturo , Fuglsang Anders , Temperini Piero , Pape Tillmann , Sofos Nicholas , Stella Stefano , Erlendsson Simon , Montoya Guillermo 
Published: June 2021

AbstractCRISPR-CasΦ is a novel family of miniaturized RNA-guided endonucleases from phages 1,2. These novel ribonucleoproteins (RNPs) provide a compact scaffold gathering all key activities of a genome editing tool2. Here, we provide the first structural insight into CasΦ singular DNA targeting and cleavage mechanism by determining the cryoEM structure of CasΦ3 with the triple strand R-loop generated after DNA cleavage. The structure reveals the unique machinery for target unwinding to form the crRNA-DNA hybrid and cleaving the target DNA. The protospacer adjacent motif (PAM) is recognised by the target strand (T-strand) and non-target strand (NT-strand) PAM interacting domains (TPID and NPID). Unwinding occurs after insertion of the conserved α1 helix disrupting the dsDNA, thus facilitating the crRNA-DNA hybrid formation. The NT-strand is funnelled towards the RuvC catalytic site, while a long helix of TPID separates the displaced NT-strand and the crRNA-DNA hybrid avoiding DNA re-annealing. The crRNA-DNA hybrid is directed to the stop (STP) domain that splits the hybrid guiding the T-strand towards the RuvC active site. The conserved RuvC insertion of the CasΦ family is extended along the hybrid, interacting with the phosphate backbone of the crRNA. A cluster of hydrophobic residues anchors the RuvC insertion in a cavity of the STP domain. The assembly of the hybrid promotes the shortening of the RuvC insertion, thus pulling the STP towards the RuvC active site to activate catalysis. These findings illustrate why CasΦ unleashes unspecific cleavage activity, degrading ssDNA molecules after activation. Site-directed mutagenesis in key residues support CasΦ3 target DNA and non-specific ssDNA cutting mechanism. Our analysis provides new avenues to redesign the compact CRISPR-CasΦ nucleases for genome editing.
Injectable Nanoparticle-Based Hydrogels Enable the Safe and Effective Deployment of Immunostimulatory CD40 Agonist Antibodies
Authors: Correa Santiago , Gale Emily C. , Mayer Aaron T. , Xiao Zunyu , Liong Celine , Klich John H. , Brown Ryanne A. , Meany Emily L. , Saouaf Olivia M. , Maikawa Caitlin L. , Grosskopf Abigail K. , Mann Joseph L. , Idoyaga Juliana , Appel Eric A. 
Published: June 2021

AbstractWhen properly deployed, the immune system can eliminate deadly pathogens, eradicate metastatic cancers, and provide long-lasting protection from diverse diseases. Unfortunately, realizing these remarkable capabilities is inherently risky as disruption to immune homeostasis can elicit dangerous complications or autoimmune disorders. While current research is continuously expanding the arsenal of potent immunotherapeutics, there is a technological gap when it comes to controlling when, where, and how long these drugs act on the body. Here, we explore the ability of a slow-releasing injectable hydrogel depot to reduce the problematic dose-limiting toxicities of immunostimulatory CD40 agonist (CD40a) while maintaining their potent anti-cancer efficacy. We leverage a previously described polymer-nanoparticle (PNP) hydrogel system that exhibits shear-thinning and yield-stress properties that we hypothesized would improve locoregional delivery of the CD40a immunotherapy. Using PET imaging, we demonstrate that prolonged hydrogel-based delivery redistributes CD40a exposure to the tumor and the tumor draining lymph node (TdLN), thereby reducing weight loss, hepatotoxicity, and cytokine storm associated with standard treatment. Moreover, CD40a-loaded hydrogels mediate improved local cytokine induction in the TdLN and improve treatment efficacy in the B16F10 melanoma model. PNP hydrogels, therefore, represent a facile, drug-agnostic method to ameliorate immune-related adverse effects and explore locoregional delivery of immunostimulatory drugs.
Investigating the reliability of molecular estimates of evolutionary time when substitution rates and speciation rates vary
Authors: Ritchie Andrew M. , Hua Xia , Bromham Lindell 
Published: June 2021

AbstractBackgroundAn accurate timescale of evolutionary history is essential to testing hypotheses about the influence of historical events and processes, and the timescale for evolution is increasingly derived from analysis of DNA sequences. But variation in the rate of molecular evolution complicates the inference of time from DNA. Evidence is growing for numerous factors, such as life history and habitat, that are linked both to the molecular processes of mutation and fixation and to rates of macroevolutionary diversification. However, the most widely used methods rely on idealised models of rate variation, such as the uncorrelated and autocorrelated clocks, and molecular dating methods are rarely tested against complex models of rate change. One relationship that is not accounted for in molecular dating is the potential for interaction between molecular substitution rates and speciation, a relationship that has been supported by empirical studies in a growing number of taxa. If these relationships are as widespread as current evidence suggests, they may have a significant influence on molecular dates.ResultsWe simulate phylogenies and molecular sequences under three different realistic rate variation models – one in which speciation rates and substitution rates both vary but are unlinked, one in which they covary continuously and one punctuated model in which molecular change is concentrated in speciation events, using empirical case studies to parameterise realistic simulations. We test three commonly used “relaxed clock” molecular dating methods against these realistic simulations to explore the degree of error in molecular dates under each model. We find average divergence time inference errors ranging from 12% of node age for the unlinked model when reconstructed under an uncorrelated rate prior using BEAST 2, to up to 91% when sequences evolved under the punctuated model are reconstructed under an autocorrelated prior using PAML.ConclusionsWe demonstrate the potential for substantial errors in molecular dates when both speciation rates and substitution rates vary between lineages. This study highlights the need for tests of molecular dating methods against realistic models of rate variation generated from empirical parameters and known relationships.