bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tumor cell-intrinsic HFE drives glioblastoma growth
Authors: Troike Katie M. , Silver Daniel J. , Ghosh Prabar K. , Mulkearns-Hubert Erin E. , Hubert Christopher G. , Connor James R. , Fox Paul L. , Kristensen Bjarne W. , Lathia Justin D. 
Published: April 2022

AbstractBackgroundGlioblastoma (GBM) tumor cells modulate expression of iron-associated genes to enhance iron uptake from the surrounding microenvironment, driving proliferation and tumor growth. The homeostatic iron regulator (HFE) gene, encoding the iron sensing HFE protein, is upregulated in GBM and correlates with poor survival outcomes. However, the molecular mechanisms underlying these observations remain unclear. Identification of pathways for targeting iron dependence in GBM tumors is therefore a critical area of investigation.MethodsWe interrogated the impact of cell-intrinsic Hfe expression on proliferation and tumor growth through genetic loss and gain of function approaches in syngeneic mouse glioma models. We determined the expression of iron-associated genes and their relationship with survival in GBM using public datasets and identified differentially expressed pathways in Hfe knockdown cells through Nanostring transcriptional profiling.ResultsLoss of Hfe induced apoptotic cell death in vitro and inhibited tumor growth in vivo while overexpression of Hfe accelerated both proliferation and tumor growth. Analysis of iron gene signatures in Hfe knockdown cells revealed alterations in the expression of several iron-associated genes, suggesting global disruption of intracellular iron homeostasis. Analyzing differentially expressed pathways further identified oxidative stress as the top pathway upregulated with Hfe loss. Enhanced 55Fe uptake and generation of reactive oxygen species (ROS) were found with Hfe knockdown, implicating toxic iron overload resulting in apoptotic cell death.ConclusionsCollectively, these findings identify a novel role for HFE in regulating iron homeostasis in GBM tumors and provide a potential avenue for future therapeutic development.Key PointsHFE is an iron sensor that is upregulated in GBM and negatively impacts survival.HFE overexpression drives proliferation and tumor growth in vivo.Loss of HFE increases production of reactive oxygen species and induces apoptosis, extending survival in vivo.Importance of StudyDysregulation of iron metabolism is an important feature of GBM contributing to tumor growth and negatively impacting survival. The identification of key iron regulators controlling this process is therefore important for therapeutic targeting. We identify HFE as an important regulator of iron homeostasis in GBM and suggest a role for sexual dimorphism in HFE-mediated tumor iron regulation that ultimately results in differential survival outcomes. Our findings demonstrate that HFE drives tumor cell proliferation and survival in GBM and may be a viable target for modulating tumor iron flux and inducing apoptosis in tumor cells.
Predicting Dog Phenotypes from Genotypes
Authors: Bartusiak Emily R. , Barrabés Míriam , Rymbekova Aigerim , Gimbernat-Mayol Julia , López Cayetana , Barberis Lorenzo , Montserrat Daniel Mas , Giró-i-Nieto Xavier , Ioannidis Alexander G. 
Published: April 2022

AbstractWe analyze doggenotypes(i.e., positions of dog DNA sequences that often vary between different dogs) in order to predict the correspondingphenotypes(i.e., unique observed characteristics). More specifically, given chromosome data from a dog, we aim to predict the breed, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare the performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, we show that the reverse is true for height and weight regression. Finally, we evaluate the results of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences, resulting in input sequences ranging from 20 SNPs to ∼200k SNPs. In doing so, we explore the impact of using a very limited number of SNPs for prediction. Our experiments demonstrate that these phenotypes in dogs can be predicted with as few as 0.5% of randomly selected SNPs (i.e., 992 SNPs) and that dog breeds can be classified with 50% balanced accuracy with as few as 0.02% SNPs (i.e., 40 SNPs).
Larger but younger fish when growth outpaces mortality in heated ecosystem
Authors: Lindmark Max , Karlsson Malin , Gårdmark Anna 
Published: April 2022

AbstractEctotherms are predicted to “shrink” with global warming, in line with general growth models and the temperature-size rule (TSR), both predicting smaller adult sizes with warming. However, they also predict faster juvenile growth rates and thus larger size-at-age of young organisms. Hence, the result of warming on the size-structure of a population depends on the interplay between how mortality rate, juvenile- and adult growth rates are affected by warming. Here, we use two-decade long time series of biological samples from a unique enclosed bay heated by cooling water from a nearby nuclear power plant to become 5–10 °C warmer than its reference area. We used growth-increment biochronologies (12658 reconstructed length-at-age estimates from 2426 individuals) to quantify how >20 years of warming has affected body growth and size-at-age and catch data to quantify mortality rates and population size- and age structure of Eurasian perch (Perca fluviatilis). In the heated area, growth rates were faster for all sizes, and hence size-at-age was larger for all ages, compared to the reference area. While mortality rates were also higher (lowering mean age by 0.4 years), the faster growth rates lead to a 2 cm larger mean size in the heated area. Differences in the size-spectrum exponent (describing the proportion of fish by size) were less clear statistically. Our analyses reveal that mortality, in addition to plastic growth and size-responses, is a key factor determining the size structure of populations exposed to warming. Understanding the mechanisms by which warming affects the size- and age structure of populations is critical for predicting the impacts of climate change on ecological functions, interactions, and dynamics.
Slow oscillations promote long range effective communication: the key for memory consolidation in a broken down network
Authors: Niknazar Hamid , Malerba Paola , Mednick Sara C. 
Published: April 2022

AbstractOne of the most prominent and robust findings in cognitive neuroscience is the strengthening of memories during non-rapid eye movement (NREM) sleep, with slow oscillations (SOs, < 1Hz) playing a critical role in systems-level consolidation. However, NREM is a sleep period generally showing a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present a novel approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM sleep in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication, but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles, compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promote sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends a predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement on the basis of independent electrophysiological observations during sleep. This work introduces a non-invasive approach to understanding information processing during sleep, a behavioral stage whose function, until now, has been understood only after synaptic reorganization after waking from sleep. Our findings provide a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state, indicating that SOs are a gating mechanism for large-scale neural communication, which is the necessary substrate for systems consolidation and long-term memory formation.
Spatiotemporal Patterning enabled by Gene Regulatory Networks
Authors: Roy Ushasi , Singh Divyoj , Vincent Navin , Haritas Chinmay , Jolly Mohit Kumar 
Published: April 2022

AbstractSpatiotemporal pattern formation plays a key role in various biological phenomena including embryogenesis and neural network formation. Though the reaction-diffusion systems enabling pattern formation have been studied phenomenonlogically, the biomolecular mechanisms behind these processes has not been modelled in detail. Here, we study the emergence of spatiotemporal patterns due to simple synthetic commonly observed two- and three-node gene regulatory network motifs coupled with their molecular diffusion in one- and two-dimensional space. We investigate the patterns formed due to the coupling of inherent multistable and oscillatory behavior of toggle switch (two mutually repressing nodes), toggle switch with double self-activation, toggle triad (three mutually repressing nodes) and repressilator (three nodes repressing the other sequentially in a cyclic manner) with the effect of spatial diffusion of these molecules. We probe various parameter regimes corresponding to different regions of stability (monostable, multistable, oscillatory) and assess the impact of varying diffusion coefficients too. This analysis offers valuable insights into the design principles of pattern formation facilitated by these network motifs, and suggest mechanistic underpinnings of biological pattern formation.
Low-efficiency conversion of proliferative glia into induced neurons by Ascl1 in the postnatal mouse cerebral cortex in vivo
Authors: Galante Chiara , Marichal Nicolás , Schuurmans Carol , Berninger Benedikt , Péron Sophie 
Published: April 2022

ABSTRACTThe proneural transcription factor Achaete-scute complex-like 1 (Ascl1) is a major regulator of neural progenitor fate, implicated both in neurogenesis and oligodendrogliogenesis. Ascl1 has been widely used to reprogram non-neuronal cells into induced neurons. In vitro, Ascl1 induces efficient reprogramming of proliferative astroglia from the early postnatal cerebral cortex into interneuron-like cells. Here, we examined whether Ascl1 can similarly induce neuronal reprogramming of glia undergoing proliferation in the postnatal mouse cerebral cortex in vivo. Toward this, we targeted cortical glia at the peak of proliferative expansion (i.e., postnatal day 5) by injecting a retrovirus encoding for Ascl1 into the mouse cerebral cortex. In sharp contrast to the very efficient reprogramming in vitro, Ascl1-transduced glial cells were converted into doublecortin-immunoreactive neurons only with low efficiency in vivo. Interfering with the phosphorylation of Ascl1 by mutation of six conserved proline-directed serine/threonine phosphorylation sites (Ascl1SA6) has been previously shown to increase its neurogenic activity in the early embryonic cerebral cortex. We therefore tested whether transduction of proliferative glia with a retrovirus encoding Ascl1SA6 improved their conversion into neurons. While in vitro glia-to-neuron conversion was markedly enhanced, in vivo reprogramming efficiency remained low. However, both wild-type and mutant Ascl1 reduced the relative number of cells expressing the astrocytic marker glial fibrillary acidic protein (GFAP) and increased the relative number of cells expressing the oligodendroglial marker Sox10 in vivo. Together, our results indicate that the enhanced neurogenic response of proliferative postnatal glia to Ascl1SA6 versus Ascl1 observed in vitro is not recapitulated in vivo.
Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter
Authors: Alamo Diego del , DeSousa Lillian , Nair Rahul M. , Rahman Suhaila , Meiler Jens , Mchaourab Hassane S. 
Published: April 2022

ABSTRACTThe Amino Acid-Polyamine-Organocation transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular GABA. Its structure, determined exclusively in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold of a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin reuptake transporter SERT and the glucose-sodium symporter transporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follows the blueprint of these or other well-studied LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced AlphaFold2 methodology. Our experimental results reveal acid-induced conformational changes that dislodge the C-terminus from the permeation pathway coupled with rearrangement of helices that enable isomerization between both inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminate the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different functional families in the LeuT-fold.SIGNIFICANCE STATEMENTThe transporter GadC contributes to acid resistance in bacterial pathogens by exchanging two substrates, glutamate and GABA, using a mechanism termed alternating access. In this study, the conformational dynamics underlying alternating access was studied using a combination of spectroscopy and computational modeling. A conformationally diverse ensemble of models, generated using AlphaFold2, guided the design and interpretation of double electron-electron resonance spectroscopy experiments. We found that whereas GadC was inactive and conformationally homogeneous at neutral pH, low pH induced isomerization between two conformations. From our integrated computational/experimental investigation emerges a transport model that may be relevant to eukaryotic homologs that are involved in other cellular processes.
CellDrift: Inferring Perturbation Responses in Temporally-Sampled Single Cell Data
Authors: Jin Kang , Schnell Daniel , Li Guangyuan , Salomonis Nathan , Surya Prasath V. B. , Szczesniak Rhonda , Aronow Bruce J. 
Published: April 2022

AbstractCells and tissues respond to perturbations in multiple ways that can be sensitively reflected in alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories, and outcomes.
Similarity-Based Multimodal Regression
Authors: Chen Andrew A. , Weinstein Sarah M. , Adebimpe Azeez , Gur Ruben C. , Gur Raquel E. , Merikangas Kathleen Ries , Satterthwaite Theodore D. , Shinohara Russell T. , Shou Haochang 
Published: April 2022

AbstractTo better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distancebased method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations in multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.
Lateral Orbitofrontal Cortex and Basolateral Amygdala Regulate Sensitivity to Delayed Punishment during Decision-making
Authors: Liley Anna E. , Gabriel Daniel B. K. , Simon Nicholas W. 
Published: April 2022

AbstractIn real-world decision-making scenarios, negative consequences do not always occur immediately after a choice. This delay between action and outcome drives the underestimation, or “delay discounting”, of punishment. While the neural substrates underlying sensitivity to immediate punishment have been well-studied, there has been minimal investigation of delayed consequences. Here, we assessed the role of lateral orbitofrontal cortex (LOFC) and basolateral amygdala (BLA), two regions implicated in cost/benefit decision-making, in sensitivity to delayed vs immediate punishment. The delayed punishment decision-making task (DPDT) was used to measure delay discounting of punishment in rodents. During DPDT, rats choose between a small, single pellet reward and a large, three pellet reward accompanied by a mild foot shock. As the task progresses, the shock is preceded by a delay that systematically increases or decreases throughout the session. We observed that rats avoid choices associated with immediate punishment, then shift preference toward these options when the punishment is delayed. LOFC inactivation did not influence choice of rewards with immediate punishment, but decreased choice of delayed punishment. We also found that BLA inactivation reduced choice of delayed punishment, but this was only evident when punishment was initially delivered immediately after a choice, then preceded by a delay as the task progressed. Therefore, both LOFC and BLA contribute to the delay discounting of punishment, and may serve as promising therapeutic targets to improve sensitivity to delayed punishment during decision-making.Significance StatementNegative consequences occurring after a delay are often underestimated, which can lead to maladaptive decision-making. While sensitivity to immediate punishment during reward-seeking has been well-studied, the neural substrates underlying sensitivity to delayed punishment remain unclear. Here, we used the Delayed Punishment Decision-making Task to determine that lateral orbitofrontal cortex and basolateral amygdala both regulate the discounting of delayed punishment, suggesting that these regions may be potential targets to improve decision-making in psychopathology.
Fast Kernel-based Association Testing of non-linear genetic effects for Biobank-scale data
Authors: Fu Boyang , Pazokitoroudi Ali , Sudarshan Mukund , Subramanian Lakshminarayanan , Sankararaman Sriram 
Published: April 2022

AbstractOur knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a powerful approach to test for nonlinear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a Kernel-based approach that can test for non-linear effects of a set of variants on a trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of individuals. We applied FastKAST to thirty quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with nonlinear effects at genome-wide significance.
Effect of Positive Allosteric Modulation and Orthosteric Agonism of Dopamine D2 Receptors on Respiration in Mouse Models of Rett Syndrome
Authors: Maletz Sebastian N. , Reid Brandon T. , Baekey David M. , Whitaker-Fornek Jessica R. , Bateman Jordan T. , Bissonnette John M. , Levitt Erica S. 
Published: April 2022

AbstractRett syndrome (RTT) is an autism spectrum disorder caused by loss-of-function mutations in the methyl-CPG-binding protein 2 (Mecp2) gene. Frequent apneas and irregular breathing are prevalent in RTT, and also occur in rodent models of the disorder, including Mecp2Bird and Mecp2R168X mice. Sarizotan, a serotonin 5-HT1a and dopamine D2-like receptor agonist, reduces the incidence of apneas and irregular breathing in mouse models of RTT (Abdala et al., 2014). Targeting the 5HT1a receptor alone also improves respiration in RTT mice (Levitt et al., 2013). However, the contribution of D2 receptors in correcting these respiratory disturbances remains untested. PAOPA, a dopamine D2 receptor positive allosteric modulator, and quinpirole, a dopamine D2 receptor orthosteric agonist, were used in conjunction with whole-body plethysmography to evaluate whether activation of D2 receptors is sufficient to improve breathing disturbances in female heterozygous Mecp2Bird/+ and Mecp2R168X/+ mice. PAOPA did not significantly change apnea incidence or irregularity score in RTT mice. PAOPA also had no effect on the ventilatory response to hypercapnia (7% CO2). In contrast, quinpirole reduced apnea incidence and irregularity scores and improved the hypercapnic ventilatory response in Mecp2R168X/+ and Mecp2Bird/+ mice, while also reducing respiratory rate. These results suggest that D2 receptors do contribute to the positive effects of sarizotan in the correction of respiratory abnormalities in Rett syndrome. However, positive allosteric modulation of the D2 receptor alone is not sufficient to evoke these effects.