bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endogenous p53 expression in human and mouse is not regulated by its 3′UTR
Authors: Mitschka Sibylle , Mayr Christine 
Published: November 2020

AbstractTheTP53gene encodes the tumor suppressor p53, which is functionally inactivated in many human cancers. Numerous studies found that overexpression of specific microRNAs or RNA-binding proteins can alter p53 expression through binding tocis-regulatory elements in theTP533′ untranslated region (3′UTR). Although these studies suggested that 3′UTR-mediated p53 expression regulation could play a role in tumorigenesis or could be exploited for therapeutic purposes, they did not investigate post-transcriptional regulation of the nativeTP53gene. We used CRISPR/Cas9 to delete the human and mouse p53 3′UTRs while preserving endogenous mRNA processing. This revealed that the endogenous 3′UTR is not involved in regulating p53 mRNA or protein expression neither in steady state nor after genotoxic stress. As we were able to confirm the previously observed repressive effects of the isolated 3′UTR in reporter assays, our data highlight the importance of genetic models in the validation of post-transcriptional gene regulatory effects.
An Examination of an Enhanced Remote Sensing Method for Agent Attribution of Forest Disturbance
Authors: Worsham Hugh Marshall 
Published: November 2020

AbstractPatterns of disturbance in Sierra Nevada forests are shifting as a result of changing climate and land uses. These changes have underscored the need for a monitoring system that both detects disturbances and attributes them to different agents. Addressing this need will aid forest management and conservation decision-making, potentially enhancing forests’ resilience to changing climatic conditions. In addition, it will advance understanding of the patterns, drivers, and consequences of forest disturbance in space and time. This study proposed and evaluated an enhanced method for disturbance agent attribution. Specifically, it tested the extent to which textural information could improve the performance of an ensemble learning method in predicting the agents of disturbance from remote sensing observations. Random Forest (RF) models were developed to attribute disturbance to three primary agents (fire, harvest, and drought) in Stanislaus National Forest, California, U.S.A., between 1999 and 2015. To account for spectral behavior and topographical characteristics that regulate vegetation and disturbance dynamics, the models were trained on predictors derived from both the Landsat record and from a digital elevation model. The predictors included measurements of spectral change acquired through temporal segmentation of Landsat data; measurements of patch geometry; and a series of landscape texture metrics. The texture metrics were generated using the Grey-Level Co-Occurrence Matrix (GLCM). Two models were produced: one with GLCM texture metrics and one without. The per-class and overall accuracies of each model were evaluated with out-of-bag (OOB) observations and compared statistically to quantify the contribution of texture metrics to classification skill. Overall OOB accuracy was 72.0% for the texture-free model and 72.2% for the texture-dependent model, with no significant accuracy difference between them. Spatial patterns in prediction maps cohered with expectations, with most harvest concentrated in mid-elevation forests and fire and stress co-occurring at lower elevations. Altogether, the method yielded adequate identification of disturbance and moderate attribution accuracy for multiple disturbance agents. While textures did not contribute meaningfully to model skill, the study offers a strong foundation for future development, which should focus on improving the efficacy of the model and generalizing it for systems beyond the Central Sierra Nevada.
HPRep: Quantifying reproducibility in HiChIP and PLAC-seq datasets
Authors: Rosen Jonathan D. , Yang Yuchen , Abnousi Armen , Chen Jiawen , Song Michael , Jones Ian R. , Shen Yin , Hu Ming , Li Yun 
Published: November 2020

AbstractHiChIP and PLAC-seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-seq data.Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-seq data from mouse embryonic stem cells and mouse brain tissues, as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrates expected clustering of data which could not be achieved by existing methods developed for Hi-C data, highlighting the need of a reproducibility metric tailored to HiChIP and PLAC-seq data.
Membrane-tethered peptides derived from intracellular loops 2 and 3 of the urotensin II receptor act as allosteric biased ligands
Authors: Nassour Hassan , Hoang Tuan Anh , Martin Ryan D. , Dallagnol Juliana C. C. , Billard Étienne , Létourneau Myriam , Novellino Ettore , Carotenuto Alfonso , Allen Bruce G. , Tanny Jason C. , Fournier Alain , Hébert Terence E. , Chatenet David 
Published: November 2020

AbstractOver the last decade, the urotensinergic system has garnered significant attention as a promising new target for the treatment of various cardiovascular diseases and also cancer. Significant investment toward the development of clinically relevant UT ligands for therapeutic intervention has been made but have met little to no success to date. The UT system, which has yet to be effectively targeted, therefore remains to be therapeutically exploited. The discovery of allosteric sites that allow modulation of receptor activity will increase the searchable chemical space against a disease-relevant target. Pepducins and other lipidated peptides have been used as both mechanistic probes and potential therapeutics. Therefore, pepducins derived from the human urotensin II receptor might represent unique tools to generate signaling bias and study UT signaling networks. Two hUT-derived pepducins, derived from the second and the third intracellular loop of UT, respectively, have been synthesized and pharmacologically characterized. Our results demonstrated that hUT-Pep2 and [Trp1, Leu2]hUT-Pep3 acted as biased ago-allosteric modulators, triggered ERK1/2phosphorylation and to a lesser extent, IP1production, stimulated cell proliferation yet were devoid of contractile activity. Interestingly, both hUT-derived pepducins were able to modulate hUII- and URP-mediated contraction albeit to different extents. These new derivatives represent unique tools to reveal the intricacies of hUT signaling and also a novel avenue to design allosteric ligands selectively targeting UT signaling that could prove to be useful for the treatment of hUT-associated diseases.
Improved SILAC quantification with data independent acquisition to investigate bortezomib-induced protein degradation
Authors: Pino Lindsay K , Baeza Josue , Lauman Richard , Schilling Birgit , Garcia Benjamin A 
Published: November 2020

ABSTRACTStable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here, we develop a SILAC-DIA-MS workflow using free, open-source software. We determine empirically that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, a 26S proteasome inhibitor FDA-approved for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined differentially degraded by both acquisition methods, we find known ubiquitin-proteasome degrands such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate this workflow making SILAC-based experiments like protein turnover more sensitive.
Asymmetric requirement of Dpp/BMP morphogen dispersal in the Drosophila wing disc
Authors: Matsuda Shinya , Schaefer Jonas V. , Mii Yusuke , Hori Yutaro , Bieli Dimitri , Taira Masanori , Plückthun Andreas , Affolter Markus 
Published: November 2020

SummaryHow morphogen gradients control patterning and growth in developing tissues remains largely unknown due to lack of tools manipulating morphogen gradients. Here, we generate two membrane-tethered protein binders that manipulate different aspects of Decapentaplegic (Dpp), a morphogen required for overall patterning and growth of the Drosophila wing. “HA trap” is based on a single-chain variable fragment (scFv) against the HA tag that traps HA-Dpp to mainly block its dispersal, while “Dpp trap” is based on a Designed Ankyrin Repeat Protein (DARPin) against Dpp that traps Dpp to block both its dispersal and signaling. Using these tools, we found that, while posterior patterning and growth require Dpp dispersal, anterior patterning and growth largely proceed without Dpp dispersal. We show thatdpptranscriptional refinement from an initially uniform to a localized expression and persistent signaling in transientdppsource cells render the anterior compartment robust against the absence of Dpp dispersal. Furthermore, despite a critical requirement ofdppfor the overall wing growth, neither Dpp dispersal nor direct signaling is critical for lateral wing growth after wing pouch specification. These results challenge the long-standing dogma that Dpp dispersal is strictly required to control and coordinate overall wing patterning and growth.
Spatial correlations and distribution of competence gene expression in biofilms ofStreptococcus mutans
Authors: Ishkov Ivan P. , Kaspar Justin R. , Hagen Stephen J. 
Published: November 2020

ABSTRACTStreptococcus mutansis an important pathogen in the human oral biofilm. It expresses virulent behaviors that are linked to its genetic competence regulon, which is controlled bycomX. Expression ofcomXis modulated by two diffusible signaling peptides, denoted CSP and XIP, and by other environmental cues such as pH and oxidative stress. The sensitivity ofS. mutanscompetence to environmental inputs that may vary on microscopic length scales raises the question of whether the biofilm environment causes spatial clustering ofS. mutansvirulence behaviors, by creating microniches where competence and related phenotypes are concentrated. We have used two-photon microscopy to characterize the spatial distribution ofcomXexpression among individualS. mutanscells in biofilms. By analyzing correlations incomXactivity, we test for spatial clustering that may suggest localized, competence microenvironments. Our data indicate that both competence-signaling peptides diffuse efficiently through the biofilm. CSP triggers a Poisson-like, spatially random,comXresponse from a subpopulation of cells that is homogeneously dispersed. XIP elicits a population-wide response. Our data indicate that competence microenvironments if they exist are small enough that the phenotypes of individual cells are not clustered or correlated to any greater extent than occurs in planktonic cultures.
Integrating long-range regulatory interactions to predict gene expression using graph convolutional networks
Authors: Bigness Jeremy , Loinaz Xavier , Patel Shalin , Larschan Erica , Singh Ritambhara 
Published: November 2020

AbstractLong-range spatial interactions among genomic regions are critical for regulating gene expression, and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying genomic organization. To address these limitations, we present GC-MERGE, aGraphConvolutionalModel forEpigeneticRegulation ofGeneExpression. Using a graph-based framework, the model incorporates important information about long-range interactions via a natural encoding of spatial interactions into the graph representation. It integrates measurements of both the spatial genomic organization and local regulatory factors, specifically histone modifications, to not only predict the expression of a given gene of interest but also quantify the importance of its regulatory factors. We apply GC-MERGE to datasets for three cell lines - GM12878 (lymphoblastoid), K562 (myelogenous leukemia), and HUVEC (human umbilical vein endothelial) - and demonstrate its state-of-the-art predictive performance. Crucially, we show that our model is interpretable in terms of the observed biological regulatory factors, high-lighting both the histone modifications and the interacting genomic regions contributing to a gene’s predicted expression. We provide model explanations for multiple exemplar genes and validate them with evidence from the literature. Our model presents a novel setup for predicting gene expression by integrating multimodal datasets in a graph convolutional framework. More importantly, it enables interpretation of the biological mechanisms driving the model’s predictions. Available at:https://github.com/rsinghlab/GC-MERGE.
DNA barcodes and reliable molecular identifications in a diverse group of invasive pests: lessons fromBactrocerafruit flies on variation across the COI gene, introgression, and standardization
Authors: Doorenweerd Camiel , Jose Michael San , Leblanc Luc , Barr Norman , Geib Scott , Chung Arthur Y.C. , Dupuis Julian , Ekayanti Arni , Fiegalan Elaida R. , Hemachandra K.S. , Hossain M. Aftab , Huang Chia-Lung , Hsu Yu-Feng , Morris Kimberly Y. , Mustapeng Andi Maryani , Niogret Jerome , Pham Thai Hong , Sirisena U.G.A.I. , Rubinoff Daniel 
Published: November 2020

AbstractThe utility of a universal DNA ‘barcode’ fragment of 658 base pairs of theCytochrome C Oxidase I(COI) gene for the recognition of all animal species has been a widely debated topic on theoretical and practical levels. Regardless of its challenges, large amounts of COI sequence data have been produced in the last two decades. To optimally use the data towards reliable species identification will require further steps to validate the method and reference libraries. The fruit fly tribe Dacini holds about a thousand species, of which eighty are pests of economic concern, including some of the world’s foremost fruit and vegetable pests, and there are many morphologically cryptic species complexes in the tribe. Where previous studies showed limited success in using COI to identify Dacini, our results with a highly curated morphological dataset indicate high congruence between morphology and COI: 90% of the species in our 5,576 sequences, 262-species global dataset can be identified with COI alone based on a monophyly criterion. However, in some key pest species belonging to complexes that were previously thought diagnosable with COI, we found that expanded sampling and independent validation of identifications using genomic data revealed introgression of mitochondrial DNA. We find that the informative SNPs are uniformly distributed across the COI gene, and we provide recommendations for standardization. We conclude that reliable molecular identifications with COI require extensive species coverage, population sampling, and genomics-supported reference identifications before they can be validated as a “diagnostic” marker for specific groups.
Enteric glial cells respond to a dietary change in the lamina propria in a MyD88-dependent manner
Authors: Liu Zhuanzhuan , Sun Hongxiang , Liang Ming , Gao Jing , Meng Liyuan , Zheng Xingping , Wei Yanxia , Kou Yanbo , Wang Yugang 
Published: November 2020

AbstractImmune and nervous system sensing are two important ways of detecting inner and outer environmental changes. Immune cell activation in the gut can promote metabolic disorders. However, whether enteric nervous system sensing and activities are also important in metabolic syndromes is not clear. Enteric glial cells (EGCs) are thought to have sensing ability, but little is known about the potential connections between EGC and metabolic disorders. Consuming a modern Western-type high-fat low-fiber diet increases the risk of obesity. Here, we reported that dietary shift from a normal chow diet to a high-fat diet in wild-type (WT) C57BL/6 mice induced a transient emergence of glial fibrillary acidic protein (GFAP)-positive EGC network in the ileal lamina propria, accompanied by an increase of glial-derived neurotrophic factors production. Inhibition of EGC metabolic activity via gliotoxin fluorocitrate or glial-intrinsic deletion of myeloid differentiation factor 88 (Myd88) in mice blocked this dietary change-induced activity. Furthermore, we found a different role of MYD88 in glial cells versus adipocyte in diet-induced obesity. The glialMyd88knockout mice gained less body weight after HFD feeding compared to the littermate controls. In contrast, adipocyte deletion ofMyd88in mice had no impact on the weight gain but had exacerbated glucose metabolic disorders. Pharmacological interventions of glial activities by fluorocitrate prevented body weight gain in a dietary type- and glial MYD88-independent manner. Collectively, our data reveal a previously unappreciated function of EGC in sensing a dietary shift-induced perturbation and glial activities as a whole may play roles in diet-induced obesity.New & NoteworthyIt is known that obesity and its related metabolic syndrome can damage the neuronal system. However, whether the neuronal system also participates in the development of obesity is unclear. Diet is an important contributing factor to obesity. Our study reveals that consuming a high-fat diet can induce a transient enteric glial cell response via its intrinsic sensing molecule(s). Inhibiting overall glial cell activities may have an impact on the development of the metabolic syndrome.
Metabolic differentiation of co-occurring Accumulibacter clades revealed through genome-resolved metatranscriptomics
Authors: McDaniel Elizabeth A. , Moya-Flores Francisco , Beach Natalie Keene , Camejo Pamela Y. , Oyserman Ben O. , Kizaric Matthew , Khor Eng Hoe , Noguera Daniel R. , McMahon Katherine D. 
Published: November 2020

ABSTRACTNatural microbial communities consist of closely related taxa that may exhibit phenotypic differences and inhabit distinct niches. However, connecting genetic diversity to ecological properties remains a challenge in microbial ecology due to the lack of pure cultures across the microbial tree of life.‘CandidatusAccumulibacter phosphatis’ is a polyphosphate-accumulating organism that contributes to the Enhanced Biological Phosphorus Removal (EBPR) biotechnological process for removing excess phosphorus from wastewater and preventing eutrophication from downstream receiving waters. Distinct Accumulibacter clades often co-exist in full-scale wastewater treatment plants and lab-scale enrichment bioreactors, and have been hypothesized to inhabit distinct ecological niches. However, since individual strains of the Accumulibacter lineage have not been isolated in pure culture to date, these predictions have been made solely on genome-based comparisons and enrichments with varying strain composition. Here, we used genome-resolved metagenomics and metatranscriptomics to explore the activity of co-existing Accumulibacter strains in an engineered bioreactor environment. We obtained four high-quality genomes of Accumulibacter strains that were present in the bioreactor ecosystem, one of which is a completely contiguous draft genome scaffolded with long Nanopore reads. We identified core and accessory genes to investigate how gene expression patterns differed among the dominating strains. Using this approach, we were able to identify putative pathways and functions that may confer distinct functions to Accumulibacter strains and provide key functional insights into this biotechnologically significant microbial lineage.IMPORTANCE‘CandidatusAccumulibacter phosphatis’ is a model polyphosphate accumulating organism that has been studied using genome-resolved metagenomics, metatranscriptomics, and metaproteomics to understand the EBPR process. Within the Accumulibacter lineage, several similar but diverging clades are defined by shared sequence identity of the polyphosphate kinase (ppk1)locus. These clades are predicted to have key functional differences in acetate uptake rates, phage defense mechanisms, and nitrogen cycling capabilities. However, such hypotheses have largely been made based on gene-content comparisons of sequenced Accumulibacter genomes, some of which were obtained from different systems. Here, we performed time-series genome-resolved metatranscriptomics to explore gene expression patterns of co-existing Accumulibacter clades in the same bioreactor ecosystem. Our work provides an approach for elucidating ecologically relevant functions based on gene expression patterns between closely related microbial populations.
SAICAr-dependent and independent effects of ADSL deficiency on neurodevelopment
Authors: Dutto Ilaria , Gerhards Julian , Herrera Antonio , Junza Alexandra , Yanes Oscar , Boeckx Cedric , Burkhalter Martin D. , Pons Sebastian , Philipp Melanie , Lüders Jens , Stracker Travis H. 
Published: November 2020

AbstractAdenylosuccinate Lyase (ADSL) functions in thede novopurine biosynthesis pathway. ADSL deficiency (ADSLD) causes numerous neurodevelopmental pathologies, including microcephaly and autism spectrum disorder. ADSLD patients have normal purine nucleotide levels but exhibit accumulation of the dephosphorylated ADSL substrates SAICAr and S-Ado. SAICAr was implicated in the neurotoxic effects of ADSLD, although its role remains unknown. We examined the effects of ADSL depletion in human cells and found increased DNA damage signaling, that was rescued by nucleosides, and impaired primary ciliogenesis, that was rescued by reducing SAICAr. By analyzing ADSL deficient chicken and zebrafish embryos we observed impaired neurogenesis and microcephaly, and neuroprogenitor attrition in zebrafish was rescued by reducing SAICAr. Zebrafish embryos also displayed phenotypes commonly linked to ciliopathies. Our results suggest that both reduced purine levels and SAICAr accumulation contribute to neurodevelopmental pathology in ADSLD and defective ciliogenesis may influence the ADSLD phenotypic spectrum.