bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE
Authors: Chongsaritsinsuk Joann , Steigmeyer Alexandra D. , Mahoney Keira E. , Rosenfeld Mia A. , Lucas Taryn M. , Ince Deniz , Kearns Fiona L. , Battison Alexandria S. , Hollenhorst Marie A. , Shon D. Judy , Tiemeyer Katherine H. , Attah Victor , Kwon Catherine , Bertozzi Carolyn R. , Ferracane Michael J. , Amaro Rommie E. , Malaker Stacy A. 
Published: February 2023

AbstractMucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, −3, −4) decorate immune cells and act as key checkpoint inhibitors in cancer. However, their dense O-glycosylation remains enigmatic both in terms of glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present a mucinase (SmE) and demonstrate its ability to selectively cleave along the mucin glycoprotein backbone, similar to others of its kind. Unlike other mucinases, though, SmE harbors the unique ability to cleave at residues bearing extremely complex glycans which enabled improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we performed molecular dynamics (MD) simulations of TIM-3 and −4 to demonstrate how glycosylation affects structural features of these proteins. Overall, we present a powerful workflow to better understand the detailed molecular structures of the mucinome.
Mesotrode: chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice
Authors: Xiao Dongsheng , Yan Yuhao , Murphy Timothy H 
Published: February 2023

AbstractBrain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.
B cell repertoire in children with skin barrier dysfunction supports altered IgE maturation associated with allergic food sensitization
Authors: Gill Kirandeep , Moore Carolina , Nwogu Onyekachi , Kroner John W. , Chang Wan-Chi , Stevens Mariana L. , Baatyrbek kyzy Asel , Biagini Jocelyn M. , Devonshire Ashley L. , Kottyan Leah , Schwartz Justin T. , Assa’ad Amal H. , Martin Lisa J. , Andorf Sandra , Khurana Hershey Gurjit K. , Roskin Krishna M. 
Published: February 2023

AbstractThe skin is a major immune organ and skin barrier dysfunction is a major risk factor for the development of the inappropriate immune response seen in allergic disease. Skin barrier disruption alters the landscape of antigens experienced by the immune system and the downstream impacts on the antibody repertoire remain poorly characterized, particularly for the IgE isotype responsible for allergic specificity and in early life, when allergic disease is developing. In this study, we sequenced antibody gene repertoires from a large and well-characterized cohort of children with atopic dermatitis and found that food sensitization was associated with lower mutation frequencies in the IgE compartment. This trend was abrogated in children living with pets during the first year of life. These results elucidate potential molecular mechanisms underlying the protective effects of pet ownership and non-antiseptic environs reported for allergic disease, and the hygiene hypothesis more broadly. We also observed increased IgE diversity and increased isotype-switching to the IgE isotype, suggesting that B cell development, particularly isotype-switching, is heavily altered in the those with food allergen sensitizations relative to those without food allergen sensitizations. Unlike for food antigens, aeroallergen sensitization exhibited no effect on IgE mutation or diversity. Consistent patterns of antibody rearrangement were associated with food allergen sensitization in subjects with atopic dermatitis. Thus, we propose the Immune Repertoire in Atopic Disease (IRAD) score, to quantify this repertoire shift and to aid clinically in patient diagnosis and risk stratification.One Sentence SummaryFood allergic sensitization is associated with altered B cell development in children with skin barrier dysfunction.
The gene expression classifier ALLCatchR identifies B-precursor ALL subtypes and underlying developmental trajectories across age
Authors: Beder Thomas , Hansen Björn-Thore , Hartmann Alina M. , Zimmermann Johannes , Amelunxen Eric , Wolgast Nadine , Walter Wencke , Zaliova Marketa , Antić Željko , Chouvarine Philippe , Bartsch Lorenz , Barz Malwine , Bultmann Miriam , Horns Johanna , Bendig Sonja , Kässens Jan , Kaleta Christoph , Cario Gunnar , Schrappe Martin , Neumann Martin , Gökbuget Nicola , Bergmann Anke Katharina , Trka Jan , Haferlach Claudia , Brüggemann Monika , Baldus Claudia D. , Bastian Lorenz 
Published: February 2023

AbstractCurrent classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes, which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). ‘High confidence predictions’ were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained ‘unclassified’. ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared patterns of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.
The Master Database of All Possible RNA Sequences and Its Integration with RNAcmap for RNA Homology Search
Authors: Chen Ke , Litfin Thomas , Singh Jaswinder , Zhan Jian , Zhou Yaoqi 
Published: February 2023

ABSTRACTRecent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by including the noncoding RNA sequences from RNAcentral, the transcriptome assembly and metagenome assembly from MG-RAST, the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to NCBI’s nucleotide database (nt) and its subsets. The resulting MARS database (Master database of All possible RNA sequences) is 20-fold larger than NCBI’s nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSA) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of noncoding RNAs.
Algae Facade Technology in Improving Air Quality and Building Health: Analysis of Microclimate and Growth ofAspergillus niger
Authors: Algifari Muhammad Haikal , Daniel Ridzik Malky , Nofdianto  
Published: February 2023

AbstractThis research was conducted in response to concerns about room comfort and the Sick Building Syndrome (SBS) phenomenon. The focus of this study was to examine the effect of photobioreactor (PBR) based algae facade on the room microclimate and growth ofAspergillus niger. A glass simulation chamber resembling the letter “U” with a size of 40 cm long, 40 cm wide, and 40 cm high has been used for the experiment. Twenty samples ofAspergillus nigerwere tested for 11 days in four simulation chambers and were given different treatments, two rooms were installed with PBR and the other two rooms were not installed PBR (control). The room with PBR had a relatively lower temperature and humidity in the peak phase with a difference of 4.3°C and 5.5% RH compared to the control room. In addition, three out of ten molds in the PBR chamber had a growth rate of about 0.94 cm2/day and the spore production of 1.9×104CFU/ml. Meanwhile, seven out of ten molds in the control room had a growth rate of 1.4 cm2/day and an average spore production of 5.4×104CFU/ml. These results indicate that the algae facade is able to improve the microclimate of the room and suppress the growth rate ofAspergillus niger. mold in buildings.
An Empirical Study on the Motivation of Helping Behavior in Rats
Authors: Han Shu , Chen Ya-Qin , Zheng Benhuiyuan , Wang Ya-Xin , Yin Bin 
Published: February 2023

AbstractUsing rodent models to explore the motivation of helping behaviors has become a new trend in recent years. Empathy, the alleviation of personal distress, and desire for social contact have been considered motivations for rodents to engage in helping behaviors. We used 108 Sprague-Dawley rats as subjects and modified the two-chambered helping behavior experimental setup in Carvalheiro and colleagues’ study to explore the main motivations of helping behavior in rodents through three experiments. The findings suggest that (1) the desire for social contact and pursuit of an interesting environment are the primary motivations for helping behavior, regardless of the presence of a dark chamber, and (2) the alleviation of personal distress and prior experience of social contact rather than distress experience contribute to the onset and persistence of helping behavior.
PLANET: A Multi-Objective Graph Neural Network Model for Protein–Ligand Binding Affinity Prediction
Authors: Zhang Xiangying , Gao Haotian , Wang Haojie , Chen Zhihang , Zhang Zhe , Chen Xinchong , Li Yan , Qi Yifei , Wang Renxiao 
Published: February 2023

AbstractPredicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been developed in recent years to tackle this issue, but many of them merely focus on reproducing the binding affinity of known binders. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input, and it was trained through a multi-objective process with three related tasks, including deriving the protein–ligand binding affinity, protein–ligand contact map, and intra-ligand distance matrix. To serve those tasks, a large number of decoy non-binders were selected and added to the standard PDBbind data set. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to other deep learning models that rely on 3D protein–ligand complex structures as input. It also showed notably better performance in virtual screening trials on the DUD-E and LIT-PCBA benchmark. In particular, PLANET achieved comparable accuracy on LIT-PCBA as the conventional docking program Glide. However, it only took less than 1% of the computation time required by Glide to finish the same job because it did not perform exhaustive conformational sampling. In summary, PLANET exhibited a decent performance in binding affinity prediction as well as virtual screening, which makes it potentially useful for drug discovery in practice.
Irreversible inhibition of TRF2TRFHrecruiting functions: a strategy to induce telomeric replication stress in cancer cells
Authors: Sobinoff Alexander P. , Di Maro Salvatore , Low Ronnie R. J. , Benedetti Rosaria , Tomassi Stefano , D’Aniello Antonia , Russo Rosita , Baglivo Ilaria , Chianese Ugo , Pedone Paolo V. , Chambery Angela , Cesare Anthony J. , Altucci Lucia , Pickett Hilda A. , Cosconati Sandro 
Published: February 2023

AbstractThe shelterin component telomeric repeat-binding factor 2 (TRF2) is an essential regulator of telomere homeostasis and genomic stability. Mutations in the TRF2TRFHdomain physically impair t-loop formation and prevent the recruitment of several factors that promote efficient telomere replication, resulting in a telomeric DNA damage response. Here, we design, synthesize, and biologically test covalent cyclic peptides that irreversibly target the TRF2TRFHdomain. We identify APOD53 as our most promising compound. APOD53 forms a covalent adduct with a reactive cysteine residue present in the TRF2TRFHdomain and induces phenotypes consistent with TRF2TRFHdomain mutants. These include induction of a telomeric DNA damage response in the absence of fusions, increased telomeric replication stress, and impaired recruitment of regulator of telomere elongation helicase 1 (RTEL1) and structure-specific endonuclease subunit (SLX4) to telomeres. We demonstrate that APOD53 impairs cell growth in both a telomerase-positive and an ALT cell line, while sparing the viability of non-cancerous cells. Finally, we find that co-treatment with APOD53 and the G4 stabilizer RHPS4 further exacerbates telomere replication stress.
Construction of a searchable database for gene expression changes in spinal cord injury experiments
Authors: Rouchka Eric C. , de Almeida Carlos , House Randi B. , Daneshmand Jonah C. , Chariker Julia H. , Saraswat-Ohri Sujata , Gomes Cynthia , Sharp Morgan , Shum-Siu Alice , Cesarz Greta M. , Petruska Jeffrey C. , Magnuson David S.K. 
Published: February 2023

ABSTRACTSpinal cord injury (SCI) is a debilitating disease resulting in an estimated 18,000 new cases in the United States on an annual basis. Significant behavioral research on animal models has led to a large amount of data, some of which has been catalogued in the Open Data Commons for Spinal Cord Injury (ODC-SCI). More recently, high throughput sequencing experiments have been utilized to understand molecular mechanisms associated with SCI, with nearly 6,000 samples from over 90 studies available in the Sequence Read Archive. However, to date, no resource is available for efficiently mining high throughput sequencing data from SCI experiments. Therefore, we have developed a protocol for processing RNA-Seq samples from high-throughput sequencing experiments related to SCI resulting in both raw and normalized data that can be efficiently mined for comparisons across studies as well as homologous discovery across species. We have processed 1,196 publicly available RNA-seq samples from 50 bulk RNA-Seq studies across nine different species, resulting in an SQLite database that can be used by the SCI research community for further discovery. We provide both the database as well as a web-based front-end that can be used to query the database for genes of interest, differential gene expression, genes with high variance, and gene set enrichments.
PlasmidHunter: Accurate and fast prediction of plasmid sequences using gene content profile and machine learning
Authors: Tian Renmao , Imanian Behzad 
Published: February 2023

AbstractPlasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions, but they can also carry genes that make bacteria harmful to humans. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. Here, we have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter achieved high accuracies (up to 96.7%) and fast speeds in benchmark tests, outperforming other existing tools.
Missense mutations in CRX homeodomain cause dominant retinopathies through two distinct mechanisms
Authors: Zheng Yiqiao , Sun Chi , Zhang Xiaodong , Ruzycki Philip A. , Chen Shiming 
Published: February 2023

SummaryHomeodomain transcription factors (HD TFs) are instrumental to vertebrate development. Mutations in HD TFs have been linked to human diseases, but their pathogenic mechanisms remain elusive. Here we useCone-Rod Homeobox (CRX)as a model to decipher the disease-causing mechanisms of two HD mutations,p.E80Aandp.K88N, that produce severe dominant retinopathies. Through integrated analysis of molecular and functional evidencein vitroand in knock-in mouse models, we uncover two novel gain-of-function mechanisms:p.E80Aincreases CRX-mediated transactivation of canonical CRX target genes in developing photoreceptors;p.K88Nalters CRX DNA-binding specificity resulting in binding at ectopic sites and severe perturbation of CRX target gene expression. Both mechanisms produce novel retinal morphological defects and hinder photoreceptor maturation distinct from loss-of-function models. This study reveals the distinct roles of E80 and K88 residues in CRX HD regulatory functions and emphasizes the importance of transcriptional precision in normal development.
High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding
Authors: Hannum Andrew , López Mario A. , Blanco Saúl A. , Betzel Richard F. 
Published: February 2023

AbstractThe human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively-demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously-unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different pre-processing pipelines are compared in order to characterize the effects of different aspects of the production of functional connectomes (FCs) on the accuracy of subject and task classification, and to identify possible confounds.
Variability in Forest Plant Traits along the Western Ghats of India and Their Environmental Drivers at Different Resolutions
Authors: Zheng Ting , Singh Aditya , Desai Ankur R. , Krishnayya N.K. , Townsend Philip A. 
Published: February 2023

AbstractIdentifying key environmental drivers for plant functional traits is an important step to understanding and predicting ecosystem responses to a changing climate. Imaging spectroscopy offers great potential to map plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least-squares regression to map seven key foliar chemical and morphological traits using NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for six sites spanning the Western Ghats of India. We studied the variation of trait space using principal components analysis at spatial resolutions from the plot level (4m), community level (30m and 100m) to the ecosystem level (1000m). We observed a consistent pattern of trait space across different resolutions, with one axis representing the traditional leaf economic spectrum defined by foliar nitrogen concentration and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment-trait correlations across resolutions with generally higher predictive capacity of our environment-traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin showed strong environmental dependencies across sites, while calcium, sugar, and nitrogen were significantly affected by site conditions. Models incorporating site as a fixed effect explained more than 50% of the trait variance at 1000m resolution. LMA showed little dependence on both environment and site conditions, implying other factors such as species composition and perhaps site history strongly affect variation in LMA. Our results show that reliable trait-trait relationships can be identified in coarse resolution imagery, but that local scale trait-trait relationships (resolutions finer than 30m) are not sensitive to broad-scale abiotic/biotc factors.