bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: novel loci revealed by increased power and improved genome-coverage
Authors: Rönneburg T. , Zan Y. , Honaker C.F. , Siegel P.B. , Carlborg Ö. 
Published: July 2021

AbstractGenetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation, often the F2, with additional, later generation individuals being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, divergent bi-directional selection responses for 56-day body weight in the Virginia chicken lines. To achieve this, a powerful strategy was developed to make use of data from all generations (F2-F18) of an advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant and 10 additional suggestive QTL for 56-day body weight were mapped, with only two of these QTL reaching genome-wide, and one suggestive, significance in analyses of the F2 generation. Five of the significant, and four of the suggestive, QTL were among the 20 loci reaching a 20% FDR-threshold in previous analyses of data from generation F15. The novel, minor-effect QTL mapped here were generally mapped due to an overall increase in power by integrating data across generations, with minor contributions from increased genome-coverage and improved marker information content. Significant and suggestive QTL now explain >60% of the difference between the parental lines, three times more than the previously reported significant QTL. Making integrated use of all available samples from multiple generations in experimental crosses is now economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-day body weight in the Virginia chicken lines.
The amplitude of fNIRS hemodynamic response in the visual cortex unmasks autistic traits in typically developing children
Authors: Mazziotti Raffaele , Scaffei Elena , Conti Eugenia , Marchi Viviana , Rizzi Riccardo , Cioni Giovanni , Battini Roberta , Baroncelli Laura 
Published: July 2021

AbstractAutistic traits represent a continuum dimension across the population, with autism spectrum disorder (ASD) being the extreme end of the distribution. Accumulating evidence shows that neuroanatomical and neurofunctional profiles described in relatives of ASD individuals reflect an intermediate neurobiological pattern between the clinical population and healthy controls. This suggests that quantitative measures detecting autistic traits in the general population represent potential candidates for the development of biomarkers identifying early pathophysiological processes associated with ASD. Functional near-infrared spectroscopy (fNIRS) has been extensively employed to investigate neural development and function. In contrast, the potential of fNIRS to define reliable biomarkers of brain activity has been barely explored. Features of non-invasiveness, portability, ease of administration and low-operating costs make fNIRS a suitable instrument to assess brain function for differential diagnosis, follow-up, analysis of treatment outcomes and personalized medicine in several neurological conditions. Here, we introduce a novel standardized procedure with high entertaining value to measure hemodynamic responses (HDR) in the occipital cortex of adult subjects and children. We found that the variability of evoked HDR correlates with the autistic traits of children, assessed by the Autism-Spectrum Quotient. Interestingly, HDR amplitude was especially linked to social and communication features, representing the core symptoms of ASD. These findings establish a quick and easy strategy for measuring visually-evoked cortical activity with fNIRS that optimize the compliance of young subjects, setting the background for testing the diagnostic value of fNIRS visual measurements in the ASD clinical population.
Prenatal androgen treatment does not alter the firing activity of hypothalamic arcuate kisspeptin neurons in female mice
Authors: Gibson Amanda G. , Jaime Jennifer , Burger Laura L. , Moenter Suzanne M. 
Published: July 2021

AbstractNeuroendocrine control of reproduction is disrupted in many individuals with polycystic ovary syndrome, who present with increased luteinizing hormone (LH), and presumably gonadotropin-releasing hormone (GnRH), release frequency, and high androgen levels. Prenatal androgenization (PNA) recapitulates these phenotypes in primates and rodents. Female offspring of mice injected with dihydrotestosterone (DHT) on gestational D16-18 exhibit disrupted estrous cyclicity, increased LH and testosterone, and increased GnRH neuron firing rate as adults. PNA also alters the developmental trajectory of GnRH neuron firing rates, markedly blunting the prepubertal peak in firing that occurs in 3wk-old controls. GnRH neurons do not express detectable androgen receptors and are thus probably not the direct target of DHT. Rather, PNA likely alters GnRH neuronal activity by modulating upstream neurons, such as hypothalamic arcuate neurons co-expressing kisspeptin, neurokinin B (gene Tac2), and dynorphin, aka KNDy neurons. We hypothesized PNA treatment changes firing rates of KNDy neurons in a similar age-dependent manner as GnRH neurons. We conducted targeted extracellular recordings (0.5-2h) of Tac2-identified KNDy neurons from control and PNA mice at 3wks of age and in adulthood. About half of neurons were quiescent (<0.005Hz). Long-term firing rates of active cells varied, suggestive of episodic activity, but were not different among groups. Short-term burst firing was also similar. We thus reject the hypothesis that PNA alters the firing rate of KNDy neurons. This does not preclude altered neurosecretory output of KNDy neurons, involvement of other neuronal populations, or in-vivo networks as critical drivers of altered GnRH firing rates in PNA mice.Significance statementPrenatal androgenization (PNA) recapitulates key aspects of the common reproductive disorder polycystic ovary syndrome. It is postulated that disruptions in the episodic pattern of gonadotropin-releasing hormone (GnRH) secretion in part underly this disorder, yet GnRH neurons do not express androgen receptor to respond directly to elevated androgens. A population of kisspeptin, neurokinin B, and dynorphin-expressing (KNDy) neurons in the hypothalamic arcuate nucleus are thought to regulate pulsatile GnRH release and some express androgen receptor. We did not find evidence, however, that PNA altered spontaneous activity of KNDy neurons before puberty at 3wks of age or in adulthood. This suggests that PNA likely acts through other components of the broader hypothalamic network to change the patterns of GnRH release.
The joint evolution of animal movement and competition strategies
Authors: Gupte Pratik R. , Netz Christoph F. G. , Weissing Franz J. 
Published: July 2021

AbstractCompetition typically takes place in a spatial context, but eco-evolutionary models rarely address the joint evolution of movement and competition strategies. Here we investigate a spatially explicit producer-scrounger model where consumers can either forage on a heterogeneous resource landscape or steal resource items from conspecifics (kleptoparasitism). We consider three scenarios: (1) a population of foragers in the absence of kleptoparasites; (2) a population of consumers that are either specialized on foraging or on kleptoparasitism; and (3) a population of individuals that can fine-tune their behavior by switching between foraging and kleptoparasitism depending on local conditions. By means of individual-based simulations, we study the joint evolution of movement and competition strategies, and we investigate the implications on the resource landscape and the distribution of consumers over this landscape. In all scenarios and for all parameters considered, movement and competition strategies evolved rapidly and consistently across replicate simulations. The evolved movement and resource exploitation patterns differ considerably across the three scenarios. For example, foragers are attracted by conspecifics in scenario (1), while they are repelled by conspecifics in scenario (2). Generally the movement strategies of kleptoparasites differ markedly from those of foragers, but even within each class of consumers polymorphisms emerge, corresponding to pronounced differences in movement patterns. In all scenarios, the distribution of consumers over resources differs substantially from ‘ideal free’ predictions. We show that this is related to the intrinsic difficulty of moving effectively on a depleted landscape with few reliable cues for movement. Our study emphasises the advantages of a mechanistic approach when studying competition in a spatial context, and suggests how evolutionary modelling can be integrated with current work in animal movement ecology.
Host species identity shapes the diversity and structure of insect microbiota
Authors: Malacrinò Antonino 
Published: July 2021

AbstractAs for most of the life that inhabits our planet, microorganisms play an essential role in the fitness of insects, including nutrition, reproduction, defence, and many other functions. More recently, we assisted in an exponential growth of studies describing the taxonomical composition of bacterial communities across insects’ phylogeny. However, there is still an outstanding question that needs to be answered: which factors contribute most to shape insects’ microbiomes? This study tries to find an answer to this question by taking advantage of publicly available sequencing data and reanalysing over 4,000 samples of insect-associated bacterial communities under a common framework. Results suggest that insect taxonomy has a wider impact on the structure and diversity of their associated microbial communities than the other factors considered (diet, sex, life stage, sample origin, and treatment). However, when specifically testing for signatures of co-diversification of insect species and their microbiota, analyses found weak support for this, suggesting that while insect species strongly drive the structure and diversity of insect microbiota, the diversification of those microbial communities did not follow their host’s phylogeny. Furthermore, a parallel survey of the literature highlights several methodological limitations that need to be considered in future research endeavours.
A regulatory circuit motif dictates whether protein turnover fluxes are more predictive as biomarkers than protein abundances
Authors: Loriaux Paul M , Tang Ying , Hoffmann Alexander 
Published: July 2021

SummaryThe identification of prognostic biomarkers fuels personalized medicine. Here we tested two underlying, but often overlooked assumptions: 1) measurements at the steady state are sufficient for predicting the response to drug action, and 2) specifically, measurements of molecule abundances are sufficient. It is not clear that these are justified, as 1) the response results from non-linear molecular relationships, and 2) the steady state is defined by both abundance and orthogonal flux information. An experimentally validated mathematical model of the cellular response to the anti-cancer agent TRAIL was our test case. We developed a mathematical representation in which abundances and fluxes (static and kinetic network features) are largely independent, and simulated heterogeneous drug responses. Machine learning revealed predictive power, but that kinetic, not static network features were most informative. Analytical treatment of the underlying network motif identified kinetic buffering as the relevant circuit design principle. Our work suggests that network topology considerations ought to guide biomarker discovery efforts.Graphic abstractHighlights–Biomarkers are usually molecule abundances but underlying networks are dynamic–Our method allows separate consideration of heterogeneous abundances and fluxes–For the TRAIL cell death network machine learning reveals fluxes as more predictive–Network motif analyses could render biomarker discovery efforts more productiveeTOC blurbPrecision medicine relies on discovering which measurements of the steady state predict therapeutic outcome. Loriaux et al show – using a new analytical approach – that depending on the underlying molecular network, synthesis and degradation fluxes of regulatory molecules may be more predictive than their abundances. This finding reveals a flaw in an implicit but hitherto untested assumption of biomarker discovery efforts and suggests that dynamical systems modeling is useful for directing future clinical studies in precision medicine.
Cellular and behavioral effects of altered NaV1.2 sodium channel ion permeability in Scn2aK1422E mice
Authors: Echevarria-Cooper Dennis M. , Hawkins Nicole A. , Misra Sunita N. , Huffman Alexandra , Thaxton Tyler , Thompson Christopher H. , Ben-Shalom Roy , Nelson Andrew D. , Lipkin Anna M. , George Alfred L. , Bender Kevin J. , Kearney Jennifer A. 
Published: July 2021

AbstractGenetic variants in SCN2A, encoding the NaV1.2 voltage-gated sodium channel, are associated with a range of neurodevelopmental disorders with overlapping phenotypes. Some variants fit into a framework wherein gain-of-function missense variants that increase neuronal excitability lead to infantile epileptic encephalopathy, while loss-of-function variants that reduce neuronal excitability lead to developmental delay and/or autism spectrum disorder with or without co- morbid seizures. One unique case less easily classified using this binary paradigm is the de novo missense variant SCN2A p.K1422E, associated with infant-onset developmental delay, infantile spasms, and features of autism spectrum disorder. Prior structure-function studies demonstrated that K1422E substitution alters ion selectivity of NaV1.2, conferring Ca2+ permeability, lowering overall conductance, and conferring resistance to tetrodotoxin (TTX). Based on heterologous expression of K1422E, we developed a compartmental neuron model that predicted mixed effects on channel function and neuronal activity. We also generated Scn2aK1422E mice and characterized effects on neurons and neurological/neurobehavioral phenotypes. Dissociated neurons from heterozygous Scn2aK1422E/+ mice exhibited a novel TTX-resistant current with a reversal potential consistent with mixed ion permeation. Cortical slice recordings from Scn2aK1442E/+ tissue demonstrated impaired action potential initiation and larger Ca2+ transients at the axon initial segment during the rising phase of the action potential, suggesting mixed effects on channel function. Scn2aK1422E/+ mice exhibited rare spontaneous seizures, interictal EEG abnormalities, altered response to induced seizures, reduced anxiety-like behavior and alterations in olfactory-guided social behavior. Overall, Scn2aK1422E/+ mice present with phenotypes similar yet distinct from Scn2a knockout models, consistent with mixed effects of K1422E on NaV1.2 channel function.Significance StatementThe early-onset epilepsy variant SCN2A-p.K1422E displays unique biophysical properties in vitro. To model the impact of this rare variant, we generated Scn2aK1422E mice. Neurons from heterozygous Scn2aK1422E/+ mice showed functional deficits similar to the loss-of-function effects observed in the Scn2a haploinsufficiency model, as well as gain-of-function effects specific to the K1422E variant. There is also some overlap in neurobehavioral phenotypes between Scn2aK1422E/+ and Scn2a haploinsufficient mice. However, Scn2aK1422E/+ mice exhibited unique epilepsy-related phenotypes, including epileptiform events and seizures. Scn2aK1422E/+ mice serve as a useful platform to investigate phenotypic complexity of SCN2A-associated disorders.
Customization of a dada2-based pipeline for fungal Internal Transcribed Spacer 1 (ITS 1) amplicon datasets
Authors: Rolling Thierry , Zhai Bing , Frame John V. , Hohl Tobias M. , Taur Ying 
Published: July 2021

AbstractIdentification and analysis of fungal communities commonly rely on internal transcribed spacer (ITS)-based amplicon sequencing. Currently, there is no gold standard to infer and classify fungal constituents, in part since methodologies have been adapted from analyses of bacterial communities. To achieve high resolution inference of fungi in clinical samples, we customized a DADA2-based pipeline using a mock community of eleven medically relevant fungi. While DADA2 allowed the discrimination of ITS1 sequences differing by a single nucleotide, quality filtering, sequencing bias, and database selection were identified as key variables determining the accuracy of sample inference. By fine-tuning quality filtering, we decreased the number of wrongly discarded sequences attributed to Aspergillus species, Saccharomyces cerevisiae, and Candida glabrata reads. We confirmed this effect in patient samples. By adapting a wobble nucleotide in the ITS1 forward primer region, we further increased the yield of S. saccharomyces and C. glabrata sequences. Finally, we showed that a BLAST-based algorithm based on the UNITE+INSD or the NCBI NT database achieved a higher reliability in species-level taxonomic annotation than the naïve Bayesian classifier implemented in DADA2. These steps optimized a robust fungal ITS1 sequencing pipeline that, in most instances, enables species level-assignment of community members.
Single-nucleus cross-tissue molecular reference maps to decipher disease gene function
Authors: Eraslan Gokcen , Drokhlyansky Eugene , Anand Shankara , Subramanian Ayshwarya , Fiskin Evgenij , Slyper Michal , Wang Jiali , Wittenberghe Nicholas Van , Rouhana John M. , Waldman Julia , Ashenberg Orr , Dionne Danielle , Win Thet Su , Cuoco Michael S. , Kuksenko Olena , Branton Philip A. , Marshall Jamie L. , Greka Anna , Getz Gad , Segrè Ayellet V. , Aguet François , Rozenblatt-Rosen Orit , Ardlie Kristin G. , Regev Aviv 
Published: July 2021

AbstractUnderstanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.
The effect of climate change on the distribution of Canidae
Authors: Porto Lucas M. V. , Bennett David , Maestri Renan , Etienne Rampal S. 
Published: July 2021

ABSTRACTLand use by humans and climate change have been seriously affecting the distribution of species resulting in a quarter of all known mammals currently threatened with extinction. Here, we modeled the present and future potential distributions of all 36 extant Canidae species to evaluate their response to future climate scenarios. In addition, we tested if canids were likely to experience evolutionary rescue, which could allow some species to adapt to climate change. Our results suggest that global warming will cause most species to lose or maintain their ranges, while a few will have the potential to benefit from future conditions and considerably expand their geographic distributions. Some canids have the potential to experience evolutionary rescue, butAtelocynus microtisandChrysocyon brachyurusare two concerning cases that do not show this capacity to adapt given the current pace of climate change. We also reveal that most Canidae hotspot regions are outside protected areas, which may be useful for the identification of key areas for conservation.
Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition
Authors: Bouchard Catherine , Wiesner Theresa , Deschênes Andréanne , Bilodeau Anthony , Turcotte Benoît , Gagné Christian , Lavoie-Cardinal Flavie 
Published: July 2021

AbstractWe introduce a deep learning model that predicts super-resolved versions of diffraction-limited microscopy images. Our model, named Task- Assisted Generative Adversarial Network (TA-GAN), incorporates an auxiliary task (e.g. segmentation, localization) closely related to the observed biological nanostructures characterization. We evaluate how TA-GAN improves generative accuracy over unassisted methods using images acquired with different modalities such as confocal, brightfield (diffraction-limited), super-resolved stimulated emission depletion, and structured illumination microscopy. The generated synthetic resolution enhanced images show an accurate distribution of the F-actin nanostructures, replicate the nanoscale synaptic cluster morphology, allow to identify dividing S. aureus bacterial cell boundaries, and localize nanodomains in simulated images of dendritic spines. We expand the applicability of the TA-GAN to different modalities, auxiliary tasks, and online imaging assistance. Incorporated directly into the acquisition pipeline of the microscope, the TA-GAN informs the user on the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to optimize the acquisition sequence, and reduce light exposure. The TA-GAN also enables the creation of domain-adapted labeled datasets requiring minimal manual annotation, and assists microscopy users by taking online decisions regarding the choice of imaging modality and regions of interest.