bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling colicin operons as genetic arms in plasmid-genome conflicts
Authors: Sudhakari Pavithra Anantharaman , Ramisetty Bhaskar Chandra Mohan 
Published: May 2021

AbstractPlasmids are acellular propagating entities that depend, as molecular parasites, on bacteria for propagation. The conflict between the bacterial genome and the parasitic plasmids allows the emergence of ‘genetic arms’ such as Colicin (Col) operons. Endonuclease Col operons encode three proteins; an endonuclease colicin (cleaves nucleic acids), an immunity protein (inactivates its cognate colicin), and lysis protein (aids in colicin release via host cell lysis). Col operons are efficient plasmid-maintenance systems; (i) the plasmid cured cells are killed by the colicins; (ii) damaged cells lyse and release the colicins that eliminate the competitors; and (iii) the released plasmids invade new bacteria. Surprisingly, some bacterial genomes have Col operons. The eco-evolutionary drive and physiological relevance of genomic Col operons are unknown. We investigated plasmidic and genomic Col operons using sequence analyses from an eco-evolutionary perspective. We found 1,248 genomic and plasmidic colicins across 30 bacterial genera. Although 51% of the genomes harbor colicins, the majority of the genomic colicins lacked a functional lysis gene, suggesting the negative selection of lethal genes. The immunity gene of the Col operon protects the cured host, thereby eliminating the metabolic burden due to plasmid. We show mutual exclusivity of Col operons on genomes and plasmids. We propose an ‘anti-addiction’ hypothesis for genomic colicins. Using a stochastic agent-based model, we show that the genomic colicins confer an advantage to the host genome in terms of immunity to the toxin and elimination of plasmid burden. Col operons are ‘genetic arms’ that regulate the ecological interplay of bacterial genomes and plasmids.
Middle Eastern Genetic Variation Improves Clinical Annotation of the Human Genome
Authors: Ramaswamy Sathishkumar , Jain Ruchi , Naofal Maha El , Halabi Nour , Yaslam Sawsan , Taylor Alan , Tayoun Ahmad Abou 
Published: May 2021

AbstractGenetic variation in populations of Middle Eastern origin remains highly underrepresented in most comprehensive genomic databases. This underrepresentation hampers the functional annotation of the human genome and challenges accurate clinical variant interpretation. To highlight the importance of capturing genetic variation in the Middle East, we aggregated whole exome and genome sequencing data from 2116 individuals in the Middle East and established the Middle East Variation (MEV) database. Of the high-impact coding (missense and loss of function) variants in this database, 53% were absent from the most comprehensive Genome Aggregation Database (gnomAD), thus representing a unique Middle Eastern variation dataset which might directly impact clinical variant interpretation. We highlight 39 variants with minor allele frequency >1% in the MEV database that were previously reported as rare disease variants in ClinVar and the Human Gene Mutation Database (HGMD). Furthermore, the MEV database consisted of 281 putative homozygous loss of function (LoF) variants, or complete knockouts, of which 31.7% (89/281) were absent from gnomAD. This set represents either complete knockouts of 83 unique genes in reportedly healthy individuals, with implications regarding disease penetrance and expressivity, or might affect dispensable exons, thus refining the clinical annotation of those regions. Intriguingly, 24 of those genes have several clinically significant variants reported in ClinVar and/or HGMD. Our study shows that genetic variation in the Middle East improves functional annotation and clinical interpretation of the genome and emphasizes the need for expanding sequencing studies in the Middle East and other underrepresented populations.
Ecologically pre-trained RNNs explain suboptimal animal decisions
Authors: Molano-Mazon Manuel , Shao Yuxiu , Duque Daniel , Yang Guangyu Robert , Ostojic Srdjan , de la Rocha Jaime 
Published: May 2021

AbstractThe strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats can quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials, but consistently deviate from optimal behavior after error trials, when they waive the accumulated evidence. To understand this outcome-dependent gating, we first show that Recurrent Neural Networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, while RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats’ strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats’ behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics which temporarily decouples the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the suboptimal behavior observed in rats reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.
Brain age predicted using graph convolutional neural network explains developmental trajectory in preterm neonates
Authors: Liu Mengting , Kim Sharon , Duffy Ben , Yuan Shiyu , Cole James H. , Toga Arthur W. , Jahanshad Neda , Barkovich Anthony James , Xu Duan , Kim Hosung 
Published: May 2021

AbstractDramatic alterations in brain morphology, such as cortical thickness and sulcal folding, occur during the 3rd trimester of gestation which overlaps with the period of premature births. Here, we investigated the ability of the graph convolutional network (GCN) to predict brain age for preterm neonates by accounting for morphometrics measured on the cortical surface and the surface mesh topology as a sparse graph. Our findings demonstrate that GCN-based age prediction of preterm neonates (n=170; mean absolute error [MAE]: 1.06 weeks) outperformed conventional machine learning algorithms and deep learning methods that did not use topological information. We further evaluated how predicted brain age (PBA) emerges as a biologically meaningful index that characterizes the current status of brain development at the time of imaging. We hypothesized that the relative brain age (RBA; PBA minus chronological age) at scan reflects a combination of perinatal clinical factors, including preterm birth, birthweight, perinatal brain injuries, exposure to postnatal steroids, etc. We also hypothesized that RBA of neonatal scans may be associated with brain functional development in the future. To validate these hypotheses, we used general linear models. Furthermore, we established structural equation models (SEM) to determine the structural relationship between preterm birth (as a latent variable of birthweight and birth age), perinatal injuries (as a latent variable of three leading brain injuries), postnatal factors (as a latent variable of six clinical conditions), RBA at scan, and neurodevelopmental scores at 30 months. Our results suggest that low birthweight, chronic lung disease, and exposure to postnatal steroids impair cortical growth, as low RBA was significantly associated with these risks. Furthermore, RBA was associated with cognitive and language scores at 30 months. SEM analysis indicated that RBA mediated the influences of preterm birth and postnatal clinical factors, but not perinatal brain injuries, toward brain functional development at 30 months. The left middle cingulate cortex showed the most accurate prediction of brain age (MAE: 1.19 weeks), followed by left posterior and right middle cingulate cortices (1.21 weeks). These cingulate regions presented faster growth than others. RBAs of several frontal cortices significantly correlated with cognitive abilities at 30 months of age (n=50). Whereas, RBA of left Broca’s area, which is important for language production and comprehension, was associated with language functional scores. Overall, our results demonstrate the potential of the GCN in both predicting brain age and localizing regional growth that relates to postnatal factors and future neurodevelopmental outcome.
Exosomes from adipose-derived stem cells alleviate myocardial infarction via microRNA-31/FIH1/HIF-1α pathway
Authors: Zhu Dihan , Wang Yang , Thomas Miracle , McLaughlin KeAsiah , Oguljahan Babayewa , Henderson Joshua , Yang Qinglin , Chen Y. Eugene , Liu Dong 
Published: May 2021

ABSTRACTOur previous study has revealed that exosomes from adipose-derived stem cells (ASCs) promote angiogenesis in subcutaneously transplanted gels by delivery of microRNA-31 (miR-31) which targets factor inhibiting hypoxia-inducible factor-1 (FIH1) in recipient cells. Here we hypothesized that ASC exosomes alleviate ischemic diseases through miR-31/FIH1/hypoxia-inducible factor-1α (HIF-1α) signaling pathway. Exosomes from ASCs were isolated by sequential centrifugations, and were characterized with nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting analysis for exosomal markers. Results from laser imaging of ischemic mouse hindlimb revealed that exosomes enhanced the blood perfusion, and this enhancement was impaired when using miR-31-depleted exosomes. Immunohistochemistry analysis showed that administration of exosomes resulted in a higher arteriole density and larger CD31+ area in ischemic hindlimb than miR-31-delpleted exosomes. Similarly, knockdown of miR-31 in exosomes reduced the effects of the exosomes on increasing ventricular fraction shortening and CD31+ area, and on decreasing infarct size. Exosomes promoted endothelial cell migration and tube formation. These changes were attenuated when miR-31 was depleted in the exosomes or when FIH1 was overexpressed in the endothelial cells. Furthermore, the results from co-immunoprecipitation and luciferase reporter assay demonstrated that the effects of exosomes on elevating the binding of HIF-1α with co-activator p300 and enhancing HIF-1α activity were decreased when miR-31 was depleted in the exosomes or FIH1 was overexpressed. Our findings provide evidence that exosomes from ASCs promote angiogenesis in both mouse ischemic hindlimb and heart through transport of miR-31 which targets FIH1 and therefore triggers HIF-1α transcriptional activation.
A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation
Authors: Coulier Adrien , Singh Prashant , Sturrock Marc , Hellander Andreas 
Published: May 2021

AbstractQuantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference and model selection need to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects - the model fidelity, the available data, and the numerical choices for inference and model selection - interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a spatial stochastic model, a coarse-grained multiscale model, and a simple well-mixed model for several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance metric, we are able to significantly reduce the computational cost of running the pipeline.
Environmental DNA (eDNA) metabarcoding surveys extend the range of invasion for non-indigenous freshwater species in Eastern Europe
Authors: Jeunen Gert-Jan , Lipinskaya Tatsiana , Gajduchenko Helen , Golovenchik Viktoriya , Moroz Michail , Rizevsky Viktor , Semenchenko Vitaliy , Gemmell Neil J. 
Published: May 2021

AbstractActive environmental DNA (eDNA) surveillance through species-specific amplification has shown increased sensitivity in the detection of non-indigenous species (NIS) compared to traditional approaches. When many NIS are of interest, however, active surveillance decreases in cost- and time-efficiency. Passive surveillance through eDNA metabarcoding takes advantage of the complex DNA signal in environmental samples and facilitates the simultaneous detection of multiple species. While passive eDNA surveillance has previously detected NIS, comparative studies are essential to determine the ability of eDNA metabarcoding to accurately describe the range of invasion for multiple NIS versus alternative approaches. Here, we surveyed twelve sites, covering nine rivers across Belarus for NIS with three different techniques, i.e., an ichthyological, hydrobiological, and eDNA survey, whereby DNA was extracted from 500 mL surface water samples and amplified with two 16S rRNA primer assays targeting the fish and macro-invertebrate biodiversity. Nine non-indigenous fish and ten non-indigenous sediment-living macro-invertebrates were detected by traditional surveys, while seven NIS eDNA signals were picked up, including four fish, one aquatic and two sediment-living macro-invertebrates. Passive eDNA surveillance extended the range of invasion further north for two invasive fish and identified a new NIS for Belarus, the freshwater jellyfish Craspedacusta sowerbii. False-negative detections for the eDNA survey could be attributed to (i) preferential amplification of aquatic over sediment-living macro-invertebrates from surface water samples and (ii) an incomplete reference database. The evidence provided in this study recommends the implementation of both molecular-based and traditional approaches to maximize the probability of early detection of non-native organisms.
TAPIOCA: Topological Attention and Predictive Inference of Chromatin Arrangement Using Epigenetic Features
Authors: Highsmith Max , Cheng Jianlin 
Published: May 2021

AbstractChromatin conformation is an important characteristic of the genome which has been repeatedly demonstrated to play vital roles in many biological processes. Chromatin can be characterized by the presence or absence of structural motifs called topologically associated domains. The de facto strategy for determination of topologically associated domains within a cell line is the use of Hi-C sequencing data. However Hi-C sequencing data can be expensive or otherwise unavailable. Various epigenetic features have been hypothesized to contribute to the determination of chromatin conformation. Here we present TAPIOCA, a self-attention based deep learning transformer algorithm for the prediction of chromatin topology which circumvents the need for labeled Hi-C data and makes effective predictions of chromatin conformation organization using only epigenetic features. TAPIOCA outperforms prior art in established metrics of TAD prediction, while generalizing across cell lines beyond those used in training.Availabilitythe source code of TAPIOCA and training and test datasets are available at https://github.com/Max-Highsmith/TAPIOCAAuthor SummaryIn this paper we outline a machine learning approach for predicting the topological organization of chromosomes using epigenetic track data as features. By utilizing an architecture inspired by the sequence transduction transformer network we are able to effectively predict multiple metrics used to characterize topologically associated domains. Our experimental results demonstrate that once trained our algorithm can effectively predict topological organization on novel cell lines all without any exposure to original Hi-C data in test datasets.
Decoding explicit and implicit representations of health and taste attributes of foods in the human brain
Authors: Schubert Elektra , Rosenblatt Daniel , Eliby Djamila , Kashima Yoshihisa , Hogendoorn Hinze , Bode Stefan 
Published: May 2021

AbstractObesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) occurring in the first second of food cue processing to predict ratings of tastiness and healthiness. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness, whereas in Experiment 2, 89 healthy participants indicated their desire to consume snack foods, with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.
HOXB7induces STAT3-mediated transformation and lung metastasis in immortalized mammary gland NMuMG cells
Authors: Azuma Kazushi , Sakamoto Mai , Katayama Shota , Matsui Atsuka , Nakamichi Kazuya , Goshima Naoki , Watanabe Shinya , Nakayama Jun , Semba Kentaro 
Published: May 2021

AbstractThe homeobox family genes are often dysregulated in a various cancer type. ParticularlyHOXB7amplification and overexpression correlate with poor prognosis in various cancer such as gastric, pancreatic, and lung cancers. Moreover,HOXB7is known to contribute to cancer progression by promoting epithelial to mesenchymal transition, anti-cancer drug resistance, and angiogenesis. In this study, we show thatHOXB7is coamplified withERBB2in a subset of breast cancer patients and HOXB7 expression correlates with poor prognosis in HER2-positive breast cancer patients. This clinical observation is supported by the following results:HOXB7overexpression in an immortalized murine mammary gland epithelial cell line NMuMG induces cellular transformationin vitro, tumorigenesis and lung metastasis through the activation of JAK-STAT signaling.
Impaired α-tubulin re-tyrosination leads to synaptic dysfunction and is a feature of Alzheimer’s disease
Authors: Peris Leticia , Qu Xiaoyi , Soleilhac Jean-Marc , Parato Julie , Lanté Fabien , Kumar Atul , Pero Maria Elena , Martínez-Hernández José , Corrao Charlotte , Falivelli Giulia , Payet Floriane , Gory-Fauré Sylvie , Bosc Christophe , Blanca Ramírez Marian , Sproul Andrew , Brocard Jacques , Di Cara Benjamin , Delagrange Philippe , Buisson Alain , Goldberg Yves , Moutin Marie-Jo , Bartolini Francesca , Andrieux Annie 
Published: May 2021

SUMMARYIn neurons, dynamic microtubules play regulatory roles in neurotransmission and synaptic plasticity. While stable microtubules contain detyrosinated tubulin, dynamic microtubules are composed of tyrosinated tubulin, suggesting that the tubulin tyrosination/detyrosination (Tyr/deTyr) cycle modulates microtubule dynamics and synaptic function. In the Tyr/deTyr cycle, the C-terminal tyrosine of α-tubulin is re-added by tubulin-tyrosine-ligase (TTL). Here we show that TTL+/− mice exhibit decreased tyrosinated microtubules, synaptic plasticity and memory deficits, and that reduced TTL expression is a feature of sporadic and familial Alzheimer’s disease (AD), with human APPV717I neurons having less dynamic microtubules. We find that spines visited by dynamic microtubules are more resistant to Amyloidβ1-42 and that TTL, by promoting microtubule entry into spines, prevents Aβ1-42-induced spine pruning. Our results demonstrate that the Tyr/deTyr cycle regulates synaptic plasticity, is protective against spine injury, and that tubulin re-tyrosination is lost in AD, providing evidence that a defective Tyr/deTyr cycle may contribute to neurodegeneration.
Source range phylogenetic community structure can predict the outcome of avian introductions
Authors: Maitner Brian S. , Park Daniel S. , Enquist Brian J , Dlugosch Katrina M 
Published: May 2021

AbstractCompeting phylogenetic models have been proposed to explain the success of species introduced to other communities. Here, we present a study predicting the establishment success of birds introduced to Florida, Hawaii, and New Zealand using several alternative models, considering species’ phylogenetic relatedness to source and recipient range taxa, propagule pressure, and traits. We find consistent support for the predictive ability of source region phylogenetic structure. However, we find that the effects of recipient region phylogenetic structure vary in sign and magnitude depending on inclusion of source region phylogenetic structure, delineation of the recipient species pool, and the use of phylogenetic correction in the models. We argue that tests of alternative phylogenetic hypotheses including the both source and recipient community phylogenetic structure, as well as important covariates such as propagule pressure, are likely to be critical for identifying general phylogenetic patterns in introduction success, predicting future invasions, and for stimulating further exploration of the underlying mechanisms of invasibility.