bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal dynamics of base excision / single-strand break repair protein complex assembly and disassembly are modulated by the PARP1/NAD+/SIRT6 axis
Authors: Koczor Christopher A. , Saville Kate M. , Andrews Joel F. , Clark Jennifer , Fang Qingming , Li Jianfeng , Makarov Mikhail V. , Migaud Marie , Sobol Robert W. 
Published: April 2021

SUMMARYAssembly and disassembly of DNA repair protein complexes at sites of DNA damage is essential to maintain genomic integrity. We investigated factors coordinating assembly of the base excision repair (BER) proteins, DNA polymerase β (Polβ) and XRCC1, to DNA lesion sites, identifying a new role for Polβ in regulating XRCC1 disassembly from DNA repair complexes and conversely, demonstrating Polβ’s dependence on XRCC1 for complex assembly. RealPAR, a genetically-encoded probe for live cell imaging of poly(ADP-ribose) (PAR), reveals that Polβ and XRCC1 require PAR for repair complex assembly and PAR degradation for disassembly. We find that BER complex assembly is further modulated by attenuation / augmentation of NAD+biosynthesis. Finally, SIRT6 does not regulate PARP1 activation but impairs XRCC1 recruitment, leading to diminished Polβ abundance at sites of DNA damage. These findings highlight coordinated yet independent roles for both PARP1 and SIRT6 and their regulation by NAD+bioavailability to facilitate BER.
Efficient reprogramming of the heavy-chain CDR3 regions of a human antibody repertoire
Authors: Ou Tianling , He Wenhui , Quinlan Brian D. , Guo Yan , Karunadharma Pabalu , Park Hajeung , Davis-Gardner Meredith E. , Tran Mai H. , Yin Yiming , Zhang Xia , Wang Haimin , Zhong Guocai , Farzan Michael 
Published: April 2021

AbstractB cells have been engineeredex vivoto express an HIV-1 broadly neutralizing antibody (bNAb). B-cell reprograming may be scientifically and therapeutically useful, but current approaches limit B-cell repertoire diversity and disrupt the organization of the heavy-chain locus. A more diverse and physiologic B-cell repertoire targeting a key HIV-1 epitope could facilitate evaluation of vaccines designed to elicit bNAbs, help identity more potent and bioavailable bNAb variants, or directly enhance viral controlin vivo. Here we address the challenges of generating such a repertoire by replacing the heavy-chain CDR3 (HCDR3) regions of primary human B cells. To do so, we identified and utilized an uncharacterized Cas12a ortholog that recognizes PAM motifs present in human JH genes. We also optimized the design of 200 nucleotide homology-directed repair templates (HDRT) by minimizing the required 3’-5’ deletion of the HDRT-complementary strand. Using these techniques, we edited primary human B cells to express a hemagglutinin epitope tag and the HCDR3 regions of the bNAbs PG9 and CH01. Those edited with bNAb HCDR3 efficiently bound trimeric HIV-1 antigens, implying they could affinity mature in vivo in response to the same antigens. This approach generates diverse B-cell repertoires recognizing a key HIV-1 neutralizing epitope.
IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data
Authors: Heydari Tiam , Langley Matthew A. , Fisher Cynthia , Aguilar-Hidalgo Daniel , Shukla Shreya , Yachie-Kinoshita Ayako , Hughes Michael , McNagny Kelly M. , Zandstra Peter W. 
Published: April 2021

ABSTRACTThe increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs provide an opportunity to inform fundamental hypotheses in developmental programs and help accelerate the design of stem cell-based technologies. We first describe the architecture of IQCELL. Next, we apply IQCELL to a scRNA-seq dataset of early mouse T-cell development and show that it can infera prioriover 75% of causal gene interactions previously reported via decades of research. We will also show that dynamic simulations of the derived GRN qualitatively recapitulate the effects of the known gene perturbations on the T-cell developmental trajectory. IQCELL is applicable to many developmental systems and offers a versatile tool to infer, simulate, and study GRNs in biological systems. (https://gitlab.com/stemcellbioengineering/iqcell)
Barrier effects on the spatial distribution ofXylella fastidiosain Alicante, Spain
Authors: Cendoya Martina , Hubel Ana , Conesa David , Vicent Antonio 
Published: April 2021

AbstractSpatial models often assume isotropy and stationarity, implying that spatial dependence is direction invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present in the form of geographical features or disease control interventions. Despite this, the issue of non-stationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of different barriers in the distribution of the quarantine plant pathogenic bacteriumXylella fastidiosain the demarcated area in Alicante, Spain. Occurrence data from the official surveys in 2018 were analyzed with four spatial Bayesian hierarchical models: i) a stationary model representing a scenario without any control interventions or geographical features; ii) a model with mountains as physical barriers; iii) a model with a continuous or iv) discontinuous perimeter barrier as control interventions surrounding the infested area. Barriers were assumed to be totally impermeable, so they should be interpreted as areas without host plants and in which it is not possible for infected vectors or propagating plant material to pass through. Inference and prediction were performed through the integrated nested Laplace approximation methodology and the stochastic partial differential equation approach. In the stationary model the posterior mean of the spatial range was 4,030.17 m 95% CI (2,907.41, 5,563.88), meaning that host plants that are closer to an infected plant than this distance would be at risk forX. fastidiosa. This distance can be used to define the buffer zone around the infested area in Alicante. In the non-stationary models, the posterior mean of the spatial range varied from 3,860.88 m 95% CI (2,918.61, 5,212.18) in the mountain barrier model to 6,141.08 m 95% CI (4,296.32, 9,042.99) in the continuous barrier model. Compared with the stationary model, the perimeter barrier models decreased the probability ofX. fastidiosapresence in the area outside the barrier. Differences between the discontinuous and continuous barrier models showed that breaks in areas with low sampling intensity resulted in a higher probability ofX. fastidiosapresence. These results may help authorities prioritize the areas for surveillance and implementation of control measures.
KBoost: a new method to infer gene regulatory networks from gene expression data
Authors: Iglesias-Martinez Luis F. , De Kegel Barbara , Kolch Walter 
Published: April 2021

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24000 genes in less than 2 hours on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at:https://github.com/Luisiglm/KBoost.
Revisiting the recombinant history of HIV-1 group M with dynamic network community detection
Authors: Olabode Abayomi S , Ng Garway T , Wade Kaitlyn E , Salnikov Mikhail , Dick David W , Poon Art FY 
Published: April 2021

AbstractA new abundance of full-length HIV-1 genome sequences provides an opportunity to revisit the standard model of HIV-1/M diversity that clusters genomes into largely non-recombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for SIV and other HIV-1 groups. Here we develop an unsupervised non-parametric clustering approach, which does not rely on predefined non-recombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (DSBM) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial GLM,P< 8 × 10−8), compared to other reference-free recombination detection programs (GARD, RDP4 and RDP5). Applied to a representative sample ofn= 525 actual HIV-1 genomes, we determinedk= 25 as the optimal number of DSBM clusters, and used change point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and novel recombination hotspots in the HIV-1 genome, and evidence of inter-subtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative new framework for HIV-1 classification.
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes
Authors: Kawaguchi Risa Karakida , Tang Ziqi , Fischer Stephan , Rajesh Chandana , Tripathy Rohit , Koo Peter K. , Gillis Jesse 
Published: April 2021

AbstractMotivationSingle-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate.ResultsIn this study, we perform a systematic comparison of 7 scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available athttps://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.Contactjesse.gillis@utoronto.caSupplementary informationSupplementary data are available atxxxxxxonline.Key points7 scATAC-seq datasets of mouse brain are systematically compared to benchmark the efficacy of neuronal cell-type annotation from gene sets.Redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation beyond the heterogeneity of scATAC-seq data.We reannotated all scATAC-seq data for detailed cell types using robust marker genes and their meta scATAC-seq profiles are publicly available at a new Meta scATAC-seq server.Predicted profiles from only DNA sequence using a deep neural network are visualized together to explore sequence-dependent and -independent epigenetic regulation.
A Comprehensive Phylogenetic and Bioinformatics Survey of Lectins in the Fungal kingdom
Authors: Lebreton Annie , Bonnardel François , Dai Yu-Cheng , Imberty Anne , Martin Francis M. , Lisacek Frédérique 
Published: April 2021

AbstractFungal lectins are a large family of glycan-binding proteins, with no enzymatic activity. They play fundamental biological roles in the interactions of fungi with their environment and are found in many different species throughout the fungal kingdom. In particular, their contribution to defence against feeders has been emphasized and extracellular lectins may be involved in the recognition of bacteria, fungal competitors and specific host plants. Their carbohydrate specificities and quaternary structures vary widely, but evidence for an evolutionary relationship within the different classes of lectins is provided by the high degree of amino acid sequence identity shared by the different fungal lectins. The UniLectin3D database contains 194 3D structures of fungal lectins, of which 129 are characterized with their carbohydrate ligand. UniLectin3D lectin classes from all origins were used to construct 107 lectin motifs in 26 folding configurations and to screen 1,223 species deposited in the genomic portal MycoCosm of the Joint Genome Institute. The resulting 33 485 protein sequences of putative lectins are organized in MycoLec, a publicly available and searchable database. The characterization of the lectin candidates in fungal genomes is based on systematic statistics regarding potential carbohydrate ligands, protein lengths, signal peptides, relative motif positions and amino acid compositions of fungal lectins. These results shed light on the evolution of the lectin gene families.
Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow
Authors: Charkow Joshua , Röst Hannes L. 
Published: April 2021

AbstractIn bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.
A general framework to learn tertiary structure for protein sequence characterization
Authors: Gao Mu , Skolnick Jeffrey 
Published: April 2021

AbstractDuring the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.
Susceptibility of rosaceous fruits and apple cultivars to postharvest rot by Paecilomyces niveus
Authors: Wang Tristan W. , Hodge Kathie T. 
Published: April 2021

AbstractPaecilomyces rot of apples is a postharvest disease caused byPaecilomyces niveus, a problematic spoiling agent of fruit juices and derivatives. The fungus produces ascospores that can survive food processing and germinate in finished fruit products. Processing apple fruits infected with Paecilomyces rot can lead toP. niveuscontaminated juices. Because the fungus produces the mycotoxin patulin, juice spoilage byP. niveusis an important health hazard. Little is known about the disease biology and control mechanisms of this recently described postharvest disease. Following Koch’s postulates, we determined that a range of previously untested rosaceous fruits and popular apple cultivars are susceptible to Paecilomyces rot infection. We also observed that two closely related food spoiling fungi,Paecilomyces fulvusandPaecilomyces variotti, were unable to infect, cause symptoms in, or reproduce in wounded fruits. Our results highlight the unique abilities ofPaecilomyces niveusto infect a variety of fruits, produce patulin, and form highly-resistant spores capable of spoiling normally shelf-stable products.
The MLR COMPASS-like complex is required to regulate triglyceride depletion and stress response in the Drosophila fat body
Authors: Ford David J , Zraly Claudia B , Ng Jeffrey , Dingwall Andrew K 
Published: April 2021

AbstractMLR COMPASS-like complexes are highly conserved histone modifiers and enhancer regulators recruited by multiple transcription factors during differentiation and development, including lineage-determining factors, nuclear receptors, and developmental signaling pathway effectors. While the essential functions of MLR complexes during differentiation-associated transcriptional reprogramming has been well-explored, roles that these complexes play during reprogramming events in terminally differentiated cells remain understudied. We determined that the Drosophila MLR complex is required in fat body adipocytes for proper regulation of the triglyceride depletion rate during non-feeding periods, including metamorphosis and adult starvation. Transcriptome analysis revealed that the complex plays a critical role in controlling stress-related reprogramming in these cells, suggesting that the metabolic phenotypes are indirect effects of dysregulated stress transcription. Furthermore, our evidence suggests that the complex interacts with Foxo and Relish (Nf-κb) pathways and is required for proper expression of their targets. This investigation further elucidates the necessary functions of MLR complexes in regulating transcriptional reprogramming in terminally differentiated cells as well as suggests novel binding partners. Apparent roles for these complexes in the proper regulation of stress-induced transcription implies new mechanisms involved in cancer and other human diseases associated with MLR subunit mutation.
Quantitative prey species detection in predator guts across multiple trophic levels by DNA shotgun sequencing
Authors: Paula Débora P. , Timbó Renata V. , Togawa Roberto C. , Vogler Alfried P. , Andow David A. 
Published: April 2021

AbstractMetabarcoding has revolutionized the study of ecological communities, but PCR bias hampers quantitative analyses, as required in studies of trophic interactions. Direct DNA shotgun sequencing can avoid the amplification step when unassembled reads are mapped to a reference database, enabling identification and quantification of prey items.Two feeding bioassays tested the precision and accuracy of quantitative assessments with the coccinellids Harmonia axyridis and Hippodamia convergens, the chrysopid Chrysoperla externa, and the aphid Myzus persicae. Guts were dissected and the total DNA extracted was directly sent to sequence by Illumina HiSeq2500 (insert 350 bp, PE 250). Predator gut content reads were blasted against an arthropod mitochondrial DNA reference database for taxonomic assignment and the matches curated for false positive prey identification using a series of bioinformatics pipelines, mostly in R. Taxonomic assignment through KrakenUnique, which is based on the counts of unique k-mers, was compared.In a Prey Quantity bioassay, the number of prey reads was correlated to the amount of prey consumed and the elapsed time since consumption. In a Direct and Indirect Predation bioassay, prey was detectable in the predator 6 h after feeding (direct predation) and the prey’s prior prey was detectable 3 h after feeding (indirect predation). Detection of indirect predation was related to the species-specific decay rates of the two predators, but not to the order of predation.We demonstrate that degraded prey DNA was quantifiable across trophic levels with high accuracy (98.4% positive predictive value) and taxonomic resolution.