bluuebunny/crossref_metadata_2025_split huggingface dataset:
Breeding with Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance
Authors: Merrick Lance F. , Burke Adrienne B. , Chen Xianming , Carter Arron H. 
Published: May 2021

AbstractMost disease resistance in plants is quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs needing to select both major and minor genes for resistance. In this experiment, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type and disease severity. We compared two types of training populations composed of 2,630 breeding lines phenotyped in single plot trials from four years (2016-2020) and 475 diversity panel lines from four years (2013-2016), both across two locations. We also compared the accuracy of models with four different major gene markers and genome-wide association (GWAS) markers as fixed effects. The prediction models used 31,975 markers replicated 50 times using 5-fold cross-validation. We then compared the GS models with marker-assisted selection to compare the prediction accuracy of the markers alone and in combination. The GS models had higher accuracies than marker-assisted selection and reached an accuracy of 0.72 for disease severity. The major gene and GWAS markers had only a small to zero increase in prediction accuracy over the base GS model, with the highest accuracy increase of 0.03 for major markers and 0.06 for GWAS markers. There was a statistical increase in accuracy by using the disease severity trait, the breeding lines, population type, and by combing years. There was also a statistical increase in accuracy using major markers within the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.
Syntaxin-1 is necessary for UNC5/Netrin-1-dependent macropinocytosis and chemorepulsion
Authors: Martínez-Mármol Ramón , Muhaisen Ashraf , Cotrufo Tiziana , Roselló-Busquets Cristina , Hernaiz-Llorens Marc , Pérez-Branguli Francesc , Andrés Rosa Maria , Ros Oriol , Pascual Marta , Ulloa Fausto , Soriano Eduardo 
Published: May 2021

AbstractBrain connectivity requires correct axonal guidance to drive axons to their appropriate targets. This process is orchestrated by guidance cues that exert attraction or repulsion to developing axons. However, the intricacies of the cellular machinery responsible for the correct response of growth cones are just being unveiled. Netrin-1 is a bifunctional molecule involved in axon pathfinding and cell migration that induces repulsion during postnatal cerebellar development. This process is mediated by Uncoordinated locomotion 5 (UNC5) receptors located on external granule layer (EGL) tracts. Here, we demonstrate that this response is characterized by enhanced membrane internalization through macropinocytosis, but not clathrin-mediated endocytosis. We show that UNC5 receptors form a protein complex with the t-SNARE syntaxin-1 (Stx1). By combining botulinum neurotoxins, a shRNA knock-down strategy and Stx1 knock-out mice, we demonstrate that this SNARE protein is required for Netrin-1-induced macropinocytosis and chemorepulsion, suggesting that Stx1 is crucial in regulating Netrin-1-mediated axonal guidance.
Tumor-secreted versican co-opts myeloid IKKβ during metastasis
Authors: Spella Magda , Lamort Anne-Sophie , Vreka Malamati , Marazioti Antonia , Lilis Ioannis , Ntaliarda Giannoula , Skiadas Georgios , Bouloukou Eleni , Giotopoulou Georgia A. , Pepe Mario A.A. , Weiss Stefanie A.I. , Petrera Agnese , Hauck Stefanie M. , Koch Ina , Lindner Michael , Hatz Rudolph A. , Behr Juergen , Arendt Kristina A.M. , Giopanou Ioanna , Brunn David , Savai Rajkumar , Jenne Dieter E. , Yull Fiona E. , Blackwell Timothy S. , Stathopoulos Georgios T. 
Published: May 2021

AbstractThe mechanisms tumor cells use to hijack the immune system are largely uncharted. Here we used bioluminescent nuclear factor (NF)-κB reporter mice and macrophages to discover that metastatic tumors trigger NF-κB activation in host macrophages, dependent on mutant KRAS signaling and delivered via secretory versican. Versican activates NF-κB in tumor-associated macrophages via inhibitor of NF-κB kinase (IKK) β, resulting in release of interleukin (IL)-1β into the tumor microenvironment. Versican silencing in cancer cells or conditional IKKβ deletion in macrophages prevents myeloid NF-κB activation and metastasis. Versican is overexpressed and/or mutated in human cancers and metastatic effusions with KRAS mutations, predicts poor survival, can aid in the development of diagnostic platforms for pleural metastasis, and is druggable via toll-like receptor (TLR) 1/2 inhibition. The data indicate a cardinal role for tumor-derived versican in establishing cross-talk with macrophage IKKβ during metastasis and may foster the development of new therapies and diagnostic tools.
Genomic comparison of eight closed genomes of multidrug resistant Salmonella enterica strains isolated from broiler farms and processing plants in Trinidad and Tobago
Authors: Maguire Meghan , Khan Anisa S. , Adesiyun Abiodun A. , Georges Karla , Gonzalez-Escalona Narjol 
Published: May 2021

AbstractSalmonella enterica is an important foodborne pathogen worldwide. We used long and short read sequencing to close genomes of eight multidrug resistant (MDR) Salmonella enterica strains, belonging to serovars Infantis (2), Albany, Oranienburg, I 4,[5],12:i:-, Javiana, Schwarzengrund, and Kentucky from broiler chicken farms and processing plants in Trinidad and Tobago. They also belonged to seven different sequence types (STs- 32, 292, 1510, 19, 24, and 96). Seven of the strains demonstrated multi-drug resistance with the presence of at least three AMR genes. Three isolates contained the quinolone resistance gene qnrB19 in plasmids (CFSAN103840, CFSAN103854, and CFSAN103872). The extended spectrum β-lactamase genes blaCTX-M-65 (CFSAN103796) and blaTEM-1 (CFSAN103852) were detected in this study. The genomes closed in this study will be useful for future source tracking and outbreak investigations in Trinidad and Tobago and worldwide.
Superscan: Supervised Single-Cell Annotation
Authors: Shasha Carolyn , Tian Yuan , Mair Florian , Miller Helen E.R. , Gottardo Raphael 
Published: May 2021

AbstractAutomated cell type annotation of single-cell RNA-seq data has the potential to significantly improve and streamline single cell data analysis, facilitating comparisons and meta-analyses. However, many of the current state-of-the-art techniques suffer from limitations, such as reliance on a single reference dataset or marker gene set, or excessive run times for large datasets. Acquiring high-quality labeled data to use as a reference can be challenging. With CITE-seq, surface protein expression of cells can be directly measured in addition to the RNA expression, facilitating cell type annotation. Here, we compiled and annotated a collection of 16 publicly available CITE-seq datasets. This data was then used as training data to develop Superscan, a supervised machine learning-based prediction model. Using our 16 reference datasets, we benchmarked Superscan and showed that it performs better in terms of both accuracy and speed when compared to other state-of-the-art cell annotation methods. Superscan is pre-trained on a collection of primarily PBMC immune datasets; however, additional data and cell types can be easily added to the training data for further improvement. Finally, we used Superscan to reanalyze a previously published dataset, demonstrating its applicability even when the dataset includes cell types that are missing from the training set.
Decoding the effects of synonymous variants
Authors: Zeng Zishuo , Aptekmann Ariel A. , Bromberg Yana 
Published: May 2021

ABSTRACTSynonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.
Challenges of γ9δ2TCR affinity maturation when using phage display
Authors: Kramer Lovro , Demuysere Margaux , van Diest Eline , Beringer Dennis X. , Kuball Jürgen 
Published: May 2021

AbstractBackgroundOver the past years we showed that the efficacy of αβT cells engineered to express a defined γδTCR (TEG) depends on the functional avidity of the γ9δ2TCR. We hypothesized that functional avidity mediated through γ9δ2TCR in the TEG format could be further enhanced by increasing affinity of the γ9δ2TCR.MethodsWe attempted to overcome limited affinity of natural occurring γ9δ2TCRs through affinity maturation by phage display using a library containing mutations in CDR1 and CDR2 of both TCR chains.ConclusionAffinity maturation of γ9δ2TCR by using phage display was not successful. The largest hurdle was the periplasmic expression of γ9δ2TCR constructs in E.coli which is a prerequisite for successful phage display. The underlying reason for this lack of expression was the instability of the single chain (sc)TCR format. Expression of scTCR formats in HEK293F cells yielded only 15-20% correctly folded scTCR.
The evolution of size-dependent competitive interactions promotes species coexistence
Authors: Anaya-Rojas Jaime M. , Bassar Ronald D. , Potter Tomos , Blanchette Allison , Callahan Shay , Framstead Nick , Reznick David , Travis Joseph 
Published: May 2021

AbstractTheory indicates that competing species coexist in a community when intraspecific competition is stronger than interspecific competition. When body size determines the outcome of competitive interactions between individuals, coexistence depends also on how resource use and the ability to compete for these resources change with body size. Testing coexistence theory in size-structured communities, therefore, requires disentangling the effects of size-dependent competitive abilities and niche shifts.Here, we tested the hypothesis that the evolution of species and size-dependent competitive asymmetries increased the likelihood of coexistence between interacting species.We experimentally estimated the effects of size-dependent competitive interactions on somatic growth rates of two interacting fish species, Trinidadian guppies (Poecilia reticulata) and killifish (Rivulus hartii). We controlled for the effects of size-dependent changes in the niche at two competitive settings representing the early (allopatric) and late (sympatric) evolutionary stages of a killifish-guppy community. We fitted the growth data to a model that incorporates species and size-dependent competitive asymmetries to test whether changes in the competitive interactions across stages increased the likelihood of species coexistence from allopatry to sympatry.We found that guppies are competitively superior to killifish but were less so in sympatric populations. The decrease in the effects of interspecific competition on the fitness of killifish and increase in the interspecific effect on guppies’ fitness increased the likelihood that sympatric guppies and killifish will coexist. However, while the competitive asymmetries between the species changed consistently between allopatry and sympatry between drainages, the size-dependent competitive asymmetries varied between drainages.These results demonstrate the importance of integrating evolution and trait-based interactions into the research of how species coexistence.
Unique molecular features and cellular responses differentiate two populations of motor cortical layer 5b neurons in a preclinical model of ALS
Authors: Moya Maria V. , Kim Rachel D. , Rao Meghana N. , Cotto Bianca A. , Pickett Sarah B. , Sferrazza Caroline E. , Heintz Nathaniel , Schmidt Eric F. 
Published: May 2021

ABSTRACTMany neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), lead to the selective degeneration of discrete cell types in the CNS despite the ubiquitous expression of many genes linked to disease. Therapeutic advancement depends on understanding unique cellular adaptations that underlie pathology of vulnerable cells in the context of disease-causing mutations. Here, we employ bacTRAP molecular profiling to elucidate cell type specific molecular responses of cortical upper motor neurons in a preclinical ALS model. Using two bacTRAP mouse lines that label distinct vulnerable or resilient projection neuron populations in motor cortex, we show that the regulation of oxidative phosphorylation (Oxphos) pathways is a common response in both cell types. However, differences in the baseline expression of genes involved in Oxphos and the handling of reactive oxygen species likely lead to the selective degeneration of the vulnerable cells. These results provide a framework to identify cell type-specific processes in neurodegenerative disease.
MOFA: Multi-Objective Flux Analysis for the COBRA Toolbox
Authors: Griesemer Marc , Navid Ali 
Published: May 2021

AbstractMulti-objective Optimization (MO) is an important tool for quantitative examination of the trade-offs faced by biological organisms. Using genome-scale constraint-based models of metabolism (GSMs), Multi-Objective Flux Analysis (MOFA) allows MO analyses of trade-offs among key biological tasks. The leading software package for conducting a plethora of different types of constraint-based analyses using GSMs is the COBRA Toolbox for MATLAB. We have developed a new add-on tool for this toolbox using Normalized Normal Constraint (NNC) that performs MOFA for a number of objectives only limited by computation power (n ≤ 10). This development will facilitate MOFA analyses by COBRA’s large user base and allow greater multi-faceted examination of metabolic trade-offs in complicated biological systems.Availability and ImplementationThe MOFA software is freely available for download from https://bbs.llnl.gov under the GPL v2 license. The program runs on MATLAB with the COBRA software on Windows, Linux, and MacOS. It includes a detailed manual explaining the input and output of a simulation, a listing of the code’s functions, and an example MOFA run using a well-curated GSM model of E. coli.Contactgriesemer1@llnl.gov or navid1@llnl.gov
Validated machine learning algorithm with sub-clonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity
Authors: Pyke Rachel Marty , Mellacheruvu Dattatreya , Dea Steven , Abbott Charles W. , Zhang Simo V. , McDaniel Lee , Levy Eric , Bartha Gabor , West John , Snyder Michael P. , Chen Richard , Boyle Sean Michael 
Published: May 2021

AbstractHuman leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens that would otherwise bind to them. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a novel machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. Through validation with cell line mixtures and patient-specific digital PCR, we demonstrate increased sensitivity compared to previously published tools and pave the way for clinical utility. Using DASH on 611 patients across 15 tumor types, we found that 18% of patients had HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (PD-L1) expression and MSI status, suggesting the HLA LOH is a key immune resistance strategy.
KIM-1-mediated anti-inflammatory activity is preserved by MUC1 induction in the proximal tubule during ischemia-reperfusion injury
Authors: Al-bataineh Mohammad M. , Kinlough Carol L. , Mi Zaichuan , Jackson Edwin K. , Mutchler Stephanie M. , Emlet David R. , Kellum John A. , Hughey Rebecca P. 
Published: May 2021

ABSTRACTCell-associated kidney injury molecule-1 (KIM-1) exerts an anti-inflammatory role following kidney injury by mediating efferocytosis and downregulating the NF-κB pathway. KIM-1 cleavage blunts its anti-inflammatory activities. We reported that Mucin 1 (MUC1) is protective in a mouse model of ischemia-reperfusion injury (IRI). As both KIM-1 and MUC1 are induced in the proximal tubule (PT) during IRI and are ADAM17 substrates, we tested the hypothesis that MUC1 protects KIM-1 activity. Muc1 KO mice and wild-type (WT) littermates were subjected to IRI. KIM-1, MUC1 and ADAM17 levels (and signaling pathways) were assessed by immunoblotting. PT localization was assessed by confocal microscopy and in situ proximity ligation assay. Findings were extended using human kidneys and urine, and KIM-1-mediated efferocytosis assays in mouse PT cultures. In response to tubular injury in mouse and human kidneys, we observed induction and co-expression of KIM-1 and MUC1 in the PT. Compared to WT, Muc1 KO mice had higher urinary KIM-1 and lower kidney KIM-1. KIM-1 was apical in PT of WT kidneys, but predominately with luminal debris in Muc1 KO mice. Efferocytosis was reduced in Muc1 KO PT cultures when compared to WT cells, while inflammation was increased in Muc1 KO kidneys when compared to WT mice. MUC1 was cleaved by ADAM17 in PT cultures, and blocked KIM-1 shedding in MDCK cells. We conclude that KIM-1-mediated efferocytosis and thus anti-inflammatory activity during IRI is preserved in the injured kidney by MUC1 inhibition of KIM-1 shedding.NEW & NOTEWORTHYKIM-1 plays a key role in the recovery of the tubule epithelium during renal IRI by mediating efferocytosis and associated signaling that suppresses inflammation. Excessive cleavage of KIM-1 by ADAM17 provides decoy receptor that aggravates efferocytosis and subsequent signaling. Our data from studies in mice, patients and cultured cells show that MUC1 is also induced during IRI and competes with KIM-1 for cleavage by ADAM17. Consequently, MUC1 protects KIM-1 anti-inflammatory activity in the damaged kidney.
Human genetic diversity alters therapeutic gene editing off-target outcomes
Authors: Cancellieri Samuele , Zeng Jing , Lin Linda Yingqi , Tognon Manuel , Nguyen My Anh , Lin Jiecong , Bombieri Nicola , Maitland Stacy A. , Ciuculescu Marioara-Felicia , Katta Varun , Tsai Shengdar Q. , Armant Myriam , Wolfe Scot A. , Giugno Rosalba , Bauer Daniel E. , Pinello Luca 
Published: May 2021

ABSTRACTCRISPR gene editing holds great promise to modify somatic genomes to ameliorate disease. In silico prediction of homologous sites coupled with biochemical evaluation of possible genomic off-targets may predict genotoxicity risk of individual gene editing reagents. However, standard computational and biochemical methods focus on reference genomes and do not consider the impact of genetic diversity on off-target potential. Here we developed a web application called CRISPRme that explicitly and efficiently integrates human genetic variant datasets with orthogonal genomic annotations to nominate and prioritize off-target sites at scale. The method considers both single-nucleotide variants (SNVs) and indels, accounts for bona fide haplotypes, accepts spacer:protospacer mismatches and bulges, and is suitable for personal genome analyses. We tested the tool with a guide RNA (gRNA) targeting theBCL11Aerythroid enhancer that has shown therapeutic promise in clinical trials for sickle cell disease (SCD) and β-thalassemia1. We find that the top candidate off-target site is produced by a non-reference allele common in African-ancestry populations (rs114518452, minor allele frequency (MAF)=4.5%) that introduces a protospacer adjacent motif (PAM) for SpCas9. We validate that SpCas9 generates indels (∼9.6% frequency) and chr2 pericentric inversions in a strictly allele-specific manner in edited CD34+ hematopoietic stem/progenitor cells (HSPCs), although a high-fidelity Cas9 variant mitigates this off-target. The CRISPRme tool highlights alternative allele-specific off-target editing as a prevalent risk of gRNAs considered for therapeutic gene editing. Our report illustrates how population and private genetic variants should be considered as modifiers of genome editing outcomes. We suggest that variant-aware off-target assessment should be considered in therapeutic genome editing efforts going forward and provide a powerful approach for comprehensive off-target nomination.
Robust Prediction of Patient-Specific Clinical Response to Unseen Drugs From
in vitro Screens Using Context-aware Deconfounding
Autoencoder
Authors: He Di , Liu Qiao , Xie Lei 
Published: May 2021

ABSTRACT
Accurate and robust prediction of patient-specific responses to drug
treatments is critical for drug development and personalized medicine. However,
patient data are often too scarce to train a generalized machine learning model.
Although many methods have been developed to utilize cell line data, few of them
can reliably predict individual patient clinical responses to new drugs due to
data distribution shift and confounding factors. We develop a novel
Context-aware Deconfounding Autoencoder (CODE-AE) that can extract common
biological signals masked by context-specific patterns and confounding factors.
Extensive studies demonstrate that CODE-AE effectively alleviates the
out-of-distribution problem for the model generalization, significantly improves
accuracy and robustness over state-of-the-art methods in both predicting
patient-specific ex vivo and in
vivo drug responses purely from in
vitro screens and disentangling intrinsic biological signals
from confounding factors. Using CODE-AE, we screened 50 drugs for 9,808 cancer
patients and discovered novel personalized anti-cancer therapies and
drug-response biomarkers.

Contact:lxie@iscb.org