bluuebunny/crossref_metadata_2025_split huggingface dataset:
Progressive myoclonic epilepsy-associated geneKctd7regulates retinal neurovascular patterning and function
Authors: Alevy Jonathan , Burger Courtney A. , Albrecht Nicholas E. , Jiang Danye , Samuel Melanie A. 
Published: May 2019

AbstractNeuron function relies on and instructs the development and precise organization of neurovascular units that in turn support circuit activity. However, our understanding of the molecular cues that regulate this relationship remains sparse. Using a high-throughput screening pipeline, we recently identified several new regulators of vascular patterning. Among these was the potassium channel tetramerization domain-containing protein 7 (KCTD7). Mutations inKCTD7are associated with progressive myoclonic epilepsy, but how KCTD7 regulates neural development and function remains poorly understood. To begin to identify such mechanisms, we focus on mouse retina, a tractable part of the central nervous system that contains precisely ordered neuron subtypes supported by a trilaminar intravascular network. We find that deletion ofKctd7results in defective patterning of the adult retina vascular network, resulting in increased branching, vessel length, and lacunarity. These alterations reflect early and specific defects in vessel development, as emergence of the superficial and deep vascular layers were delayed. These defects are likely due to a role for Kctd7 in inner retina neurons. Kctd7 it is absent from vessels but present in neurons in the inner retina, and its deletion resulted in a corresponding increase in the number of bipolar cells in development and increased vessel branching in adults. These alterations were accompanied by retinal function deficits. Together, these data suggest that neuronal Kctd7 drives growth and patterning of the vasculature and suggest that neurovascular interactions may participate in the pathogenesis of KCTD7-related human diseases.Alevy et al. HighlightsKctd7 is required for normal retinal vascular organization and retinal function in adults.Deletion ofKctd7disrupts the emergence of the superficial and deep vessel layers.Kctd7 may impact vascular patterning through influencing neurons as it is expressed in and regulates bipolar cells.Kctd7 driven neurovascular interactions may participate in the pathogenesis of KCTD7-related human diseases.Lay SummaryNeuron function requires an organized vasculature to maintain brain health and prevent disease, but many neurovasculature regulatory genes remain unknown. Alevy et al. identify the progressive myoclonic epilepsy-associated geneKctd7as a key regulator of vascular development and retinal function. They further show that Kctd7 regulation of vessel patterning likely occurs downstream of its role in regulating the development or activity of specific neuron types. These data suggest that KCTD7-regulated neurovascular interactions may participate in the pathogenesis of associated human diseases.
Nod-like receptors are critical for gut-brain axis signaling
Authors: Pusceddu Matteo M. , Barboza Mariana , Schneider Melinda , Stokes Patricia , Sladek Jessica A. , Torres-Fuentes Cristina , Goldfild Lily R. , Gillis Shane E. , Brust-Mascher Ingrid , Rabasa Gonzalo , Wong Kyle A. , Lebrilla Carlito , Byndloss Mariana X. , Maisonneuve Charles , Bäumler Andreas J. , Philpott Dana J. , Ferrero Richard , Barrett Kim E. , Reardon Colin , Gareau Mélanie G. 
Published: May 2019

ABSTRACTGut-brain axis signaling is critical for maintaining health and homeostasis. Stressful life events can impact gut-brain signaling, leading to altered mood, cognition and intestinal dysfunction. Here we identify nucleotide binding oligomerization domain (Nod)-like receptors (NLR), Nod1 and Nod2, as novel regulators for gut-brain signaling. NLR are innate immune pattern recognition receptors expressed in the gut and brain, important in the regulation of gastrointestinal (GI) physiology. We found that mice deficient in both Nod1 and Nod2 (NodDKO) demonstrate signs of stress-induced anxiety, cognitive impairment and depression in the context of a hyperactive hypothalamic-pituitary-adrenal axis. These deficits were coupled with impairments in the serotonergic pathway in the brain, decreased hippocampal neurogenesis, and reduced neural activation. In addition, NodDKO mice had increased GI permeability and altered serotonin signaling in the gut following exposure to acute stress. Administration of the selective serotonin reuptake inhibitor, fluoxetine, abrogated behavioral impairments and restored serotonin signaling. We also identified that intestinal epithelial cell-specific deletion of Nod1 (VilCre+Nod1f/f), but not Nod2, increased susceptibility to stress-induced anxiety-like behavior and cognitive impairment following exposure to stress. Together these data suggest that intestinal epithelial NLR are novel modulators of gut-brain communication and may serve as potential novel therapeutic targets for the treatment of gut-brain disorders.
Deconstructingtaxa x taxa x environmentinteractions in the microbiota: A theoretical examination
Authors: Yitbarek Senay , Guittar John , Knutie Sarah A. , Ogbunugafor C. Brandon 
Published: May 2019

AbstractA major objective of microbial ecology is to identify how the composition of gut microbial taxa shapes host phenotypes. However, most studies focus solely on community-level patterns and pairwise interactions and ignore the potentially significant effects of higher-order interactions involving three or more component taxa.Studies on higher-order interactions among microbial taxa are scarce for many reasons, including experimental intractability, daunting diversity and complexity of many microbial systems, and the potential confounding role of the environment. Moreover, we still lack the empirical and statistical tools to isolate and understand the role of higher-order interactions on the host.Here, we apply a mathematical approach to quantifying the effects of higher-order interactions among taxa on host infection risk. To do so, we adapt the Hadamard-Walsh method recently used in evolutionary genetics to quantify the nonlinear effects of mutations on fitness. We apply our approach to anin silicodataset built to resemble a population of insect hosts with gut-associated microbial communities at risk of infection from an intestinal parasite. Critically, we examine these interactions across a breadth of environmental contexts, using nutrient content of the insect diet as a model for context.We find that the effect of higher-order interactions is considerable and can change appreciably across environmental contexts. Strikingly, the relative eminence of different orders (pairwise vs. third order, fourth order, and fifth order) changes as a function of environmental context. Furthermore, we show–in our theoretical microcosm–that higher-order interactions can stabilize community structure thereby reducing host susceptibility to parasite invasion.Our approach illustrates how incorporating the effects of higher-order interactions among gut microbiota across environments can be essential for understanding their effects on host phenotypes. We conclude that higher-order interactions among taxa can profoundly shape important organismal phenotypes, and they deserve greater attention in host-associated microbiome studies.
Comparing Alternative Computational Models of the Stroop Task Using Effective Connectivity Analysis of fMRI Data
Authors: Ketola Micah , Jiang Linxing Preston , Stocco Andrea 
Published: May 2019

AbstractMethodological advances have made it possible to generate fMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and making it possible to distinguish between alternative models that produce otherwise identical behavioral patterns. However, for tasks associated with relatively brief response times, fMRI predictions are often not sufficient to compare alternative models. In this paper, we outline a method based on effective connectivity, which significantly augments the amount of information that can be extracted from fMRI data to distinguish between models. We show the application of this method in the case of two competing ACT-R models of the Stroop task. Although the models make, predictably, identical behavioral and BOLD time-course predictions, patterns of functional connectivity favor one model over the other. Finally, we show that the same data suggests directions in which both models should be revised.
An evolutionary-based approach to quantify the genetic barrier to drug resistance in fast-evolving viruses: an application to HIV-1 subtypes and integrase inhibitors
Authors: Theys Kristof , Libin Pieter , Laethem Kristel Van , Abecasis Ana B 
Published: May 2019

AbstractViral pathogens causing global disease burdens are often characterised by high rates of evolutionary changes, facilitating escape from therapeutic or immune selective pressure. Extensive viral diversity at baseline can shorten the time to resistance emergence and alter mutational pathways, but the impact of genotypic background on the genetic barrier can be difficult to capture, in particular for antivirals in experimental stages, recently approved or expanded into new settings. We developed an evolutionary-based counting method to quantify the population genetic potential to resistance and assess differences between populations. We demonstrate its applicability to HIV-1 integrase inhibitors, as their increasing use globally contrasts with limited availability of non-B subtype resistant sequences and corresponding knowledge gap on drug resistance. A large sequence dataset encompassing most prevailing subtypes and resistance mutations of first- and second-generation inhibitors were investigated. A varying genetic potential for resistance across HIV-1 subtypes was detected for 15 mutations at 12 positions, with notably 140S in subtype B, while 140C was discarded to vary across subtypes. An additional analysis for HIV-1 reverse transcriptase inhibitors identified a higher potential for 65R in subtype C, on the basis of a differential codon usage not reported before. The evolutionary interpretation of genomic differences for antiviral treatment remains challenging. Our framework advances existing counting methods with an increased sensitivity that identified novel subtype dependencies as well as rejected previous statements. Future applications include novel HIV-1 drug classes as well as other viral pathogens.
Pseudomonasisolates degrade and form biofilms on polyethylene terephthalate (PET) plastic
Authors: Vague Morgan , Chan Gayle , Roberts Cameron , Swartz Natasja A. , Mellies Jay L. 
Published: May 2019

ABSTRACTBioaugmentation is a possible remediation strategy for the massive amounts of plastic waste in our oceans and landfills. For this study, soil samples were collected from petroleum polluted locations in the Houston, Texas area to isolate microorganisms capable of plastic degradation. Bacteria were propagated and screened for lipase activity, which has been associated with the bacterial degradation of some plastics to date. We identified three lipase-positivePseudomonasspecies, andBacillus cereusas part of two consortia, which we predict enhances biofilm formation and plastic degradation. Lipase-positive consortia bacteria were incubated alongside blank andE.colicontrols with UV-irradiated polyethylene terephthalate (PET), high-density polyethylene (HDPE), or low-density polyethylene (LDPE) as sole sources of carbon. Surface degradation of PET plastic was quantified by changes in molecular vibrations by infrared spectroscopy. The bacteria formed biofilms on PET, observed by scanning electron microscopy, and induced molecular changes on the plastic surface, indicating the initial stages of plastic degradation. We also found molecular evidence that one of thePseudomonasisolates degrades LDPE. To date, lipase positivePseudomonasspp. degradation of PET has not been well described, and this work highlights the potential for using consortia of common soil bacteria to degrade plastic waste.
TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize
Authors: Chen Qiuyue , Yang Chin Jian , York Alessandra M. , Xue Wei , Daskalska Lora L. , DeValk Craig A. , Krueger Kyle W. , Lawton Samuel B. , Spiegelberg Bailey G. , Schnell Jack M. , Neumeyer Michael A. , Perry Joseph S. , Peterson Aria C. , Kim Brandon , Bergstrom Laura , Yang Liyan , Barber Isaac C. , Tian Feng , Doebley John F. 
Published: May 2019

AbstractRecombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping population (TeoNAM), derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTLs from JLM were identified with many of these mapping to known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report thatPROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTLs for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.
Visual body form and orientation cues do not modulate visuo-tactile temporal integration
Authors: Smit Sophie , Rich Anina N. , Zopf Regine 
Published: May 2019

AbstractBody ownership relies on spatiotemporal correlations between multisensory signals and visual cues specifying oneself such as body form and orientation. The mechanism for the integration of bodily signals remains unclear. One approach to model multisensory integration that has been influential in the multisensory literature is Bayesian causal inference. This specifies that the brain integrates spatial and temporal signals coming from different modalities when it infers a common cause for inputs. As an example, the rubber hand illusion shows that visual form and orientation cues can promote the inference of a common cause (one’s body) leading tospatial integrationshown by a proprioceptive drift of the perceived location of the real hand towards the rubber hand. Recent studies investigating the effect of visual cues ontemporal integration, however, have led to conflicting findings. These could be due to task differences, variation in ecological validity of stimuli and/or small samples. In this pre-registered study, we investigated the influence of visual information on temporal integration using a visuo-tactile temporal order judgement task with realistic stimuli and a sufficiently large sample determined by Bayesian analysis. Participants viewed videos of a touch being applied to plausible or implausible visual stimuli for one’s hand (hand oriented plausibly, hand rotated 180 degrees, or a sponge) while also being touched at varying stimulus onset asynchronies. Participants judged which stimulus came first: viewed or felt touch. Results show that visual cues do not modulate visuo-tactile temporal order judgements. This is not in line with the idea that bodily signals indicating oneself influence the integration of multisensory signals in the temporal domain. The current study emphasises the importance of rigour in our methodologies and analyses to advance the understanding of how properties of multisensory events affect the encoding of temporal information in the brain.
Ecological and genomic attributes of novel bacterial taxa that thrive in subsurface soil horizons
Authors: Brewer Tess E. , Aronson Emma L. , Arogyaswamy Keshav , Billings Sharon A. , Botthoff Jon K. , Campbell Ashley N. , Dove Nicholas C. , Fairbanks Dawson , Gallery Rachel E. , Hart Stephen C. , Kaye Jason , King Gary , Logan Geoffrey , Lohse Kathleen A. , Maltz Mia R. , Mayorga Emilio , O’Neill Caitlin , Owens Sarah M. , Packman Aaron , Pett-Ridge Jennifer , Plante Alain F. , Richter Daniel D. , Silver Whendee L. , Yang Wendy H. , Fierer Noah 
Published: May 2019

AbstractWhile most bacterial and archaeal taxa living in surface soils remain undescribed, this problem is exacerbated in deeper soils owing to the unique oligotrophic conditions found in the subsurface. Additionally, previous studies of soil microbiomes have focused almost exclusively on surface soils, even though the microbes living in deeper soils also play critical roles in a wide range of biogeochemical processes. We examined soils collected from 20 distinct profiles across the U.S. to characterize the bacterial and archaeal communities that live in subsurface soils and to determine whether there are consistent changes in soil microbial communities with depth across a wide range of soil and environmental conditions. We found that bacterial and archaeal diversity generally decreased with depth, as did the degree of similarity of microbial communities to those found in surface horizons. We observed five phyla that consistently increased in relative abundance with depth across our soil profiles: Chloroflexi, Nitrospirae, Euryarchaeota, and candidate phyla GAL15 and Dormibacteraeota (formerly AD3). Leveraging the unusually high abundance of Dormibacteraeota at depth, we assembled genomes representative of this candidate phylum and identified traits that are likely to be beneficial in low nutrient environments, including the synthesis and storage of carbohydrates, the potential to use carbon monoxide (CO) as a supplemental energy source, and the ability to form spores. Together these attributes likely allow members of the candidate phylum Dormibacteraeota to flourish in deeper soils and provide insight into the survival and growth strategies employed by the microbes that thrive in oligotrophic soil environments.ImportanceSoil profiles are rarely homogeneous. Resource availability and microbial abundances typically decrease with soil depth, but microbes found in deeper horizons are still important components of terrestrial ecosystems. By studying 20 soil profiles across the U.S., we documented consistent changes in soil bacterial and archaeal communities with depth. Deeper soils harbored distinct communities compared to the more commonly studied surface horizons. Most notably, we found that the candidate phylum Dormibacteraeota (formerly AD3) was often dominant in subsurface soils, and we used genomes from uncultivated members of this group to identify why these taxa are able to thrive in such resource-limited environments. Simply digging deeper into soil can reveal a surprising amount of novel microbes with unique adaptations to oligotrophic subsurface conditions.
Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus
Authors: Figgett William A , Monaghan Katherine , Ng Milica , Alhamdoosh Monther , Maraskovsky Eugene , Wilson Nicholas J , Hoi Alberta Y , Morand Eric F , Mackay Fabienne 
Published: May 2019

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.
Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models
Authors: Esteban Marina , Peña-Chilet María , Loucera Carlos , Dopazo Joaquín 
Published: May 2019

AbstractBackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
Automated high-throughput proteome and phosphoproteome analysis using paramagnetic bead technology
Authors: Leutert Mario , Rodriguez-Mias Ricard A. , Fukuda Noelle K. , Villén Judit 
Published: May 2019

AbstractRecent developments in proteomics have enabled signaling studies where >10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase-substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid-robotic-phosphoproteomics (R2-P2), an end-to-end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry-ready phosphopeptides. R2-P2 is robust, versatile, high-throughput, and achieves higher sensitivity than classical protocols. To showcase the method, we applied it, in combination with data-independent acquisition mass spectrometry, to study signaling dynamics in the mitogen-activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high-osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large-scale signaling studies involving hundreds of perturbations opening the door to systems-level studies aiming to capture signaling complexity.