bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pathogenic variants inTMEM184Bcause a neurodevelopmental syndrome via alteration of metabolic signaling
Authors: Chapman Kimberly A , Ullah Farid , Yahiku Zachary A , Kodiparthi Sri Varsha , Kellaris Georgios , Correia Sandrina P , Stödberg Tommy , Sofokleous Christalena , Marinakis Nikolaos M , Fryssira Helena , Tsoutsou Eirini , Traeger-Synodinos Jan , Accogli Andrea , Salpietro Vincenzo , Striano Pasquale , Berger Seth I , Pond Kelvin W , Sirimulla Suman , Davis Erica E , Bhattacharya Martha RC 
Published: July 2024

ABSTRACTTransmembrane protein 184B (TMEM184B) is an endosomal 7-pass transmembrane protein with evolutionarily conserved roles in synaptic structure and axon degeneration. We report six pediatric patients who havede novoheterozygous variants inTMEM184B. All individuals harbor rare missense or mRNA splicing changes and have neurodevelopmental deficits including intellectual disability, corpus callosum hypoplasia, seizures, and/or microcephaly. TMEM184B is predicted to contain a pore domain, wherein many human disease-associated variants cluster. Structural modeling suggests that all missense variants alter TMEM184B protein stability. To understand the contribution of TMEM184B to neural developmentin vivo, we suppressed the TMEM184B ortholog in zebrafish and observed microcephaly and reduced anterior commissural neurons, aligning with patient symptoms. EctopicTMEM184Bexpression resulted in dominant effects for K184E and G162R. However,in vivocomplementation studies demonstrate that all other variants tested result in diminished protein function and indicate a haploinsufficiency basis for disease. Expression of K184E and other variants increased apoptosis in cell lines and altered nuclear localization of transcription factor EB (TFEB), a master regulator of lysosomal biogenesis, suggesting disrupted nutrient signaling pathways. Together, our data indicate that TMEM184B variants cause cellular metabolic disruption likely through divergent molecular effects that all result in abnormal neural development.
Towards a More Objective and High-throughput Spheroid Invasion Assay Quantification Method
Authors: Mungai Rozanne W. , Hartman Roger J. , Jolin Grace E. , Piskorowski Kevin W. , Billiar Kristen L. 
Published: July 2024

ABSTRACTMulticellular spheroids embedded in 3D hydrogels are prominentin vitromodels for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high□throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high□throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis.
PRDM16-DT: A Brain and Astrocyte-Specific lncRNA Implicated in Alzheimer’s Disease
Authors: Schröder Sophie , Fuchs Ulrike , Gisa Verena , Pena Tonatiuh , Krüger Dennis M , Hempel Nina , Burkhardt Susanne , Salinas Gabriela , Schütz Anna-Lena , Delalle Ivana , Sananbenesi Farahnaz , Fischer Andre 
Published: July 2024

AbstractAstrocytes provide crucial support for neurons, contributing to synaptogenesis, synaptic maintenance, and neurotransmitter recycling. Under pathological conditions, deregulation of astrocytes contributes to neurodegenerative diseases such as Alzheimer’s disease (AD), highlighting the growing interest in targeting astrocyte function to address early phases of AD pathogenesis. While most research in this field has focused on protein-coding genes, non-coding RNAs, particularly long non-coding RNAs (lncRNAs), have emerged as significant regulatory molecules. In this study, we identified the lncRNAPRDM16-DTas highly enriched in the human brain, where it is almost exclusively expressed in astrocytes.PRDM16-DTand its murine homolog,Prdm16os, are downregulated in the brains of AD patients and in AD models. In line with this, knockdown ofPRDM16-DTandPrdm16osrevealed its critical role in maintaining astrocyte homeostasis and supporting neuronal function by regulating genes essential for glutamate uptake, lactate release, and neuronal spine density through interactions with the RE1-Silencing Transcription factor (Rest) and Polycomb Repressive Complex 2 (PRC2). Notably, CRISPR-mediated overexpression ofPrdm16osmitigated functional deficits in astrocytes induced by stimuli linked to AD pathogenesis. These findings underscore the importance ofPRDM16-DTin astrocyte function and its potential as a novel therapeutic target for neurodegenerative disorders characterized by astrocyte dysfunction
SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes
Authors: Pan Tong , Bi Yue , Wang Xiaoyu , Zhang Ying , Webb Geoffrey I. , Gasser Robin B. , Kurgan Lukasz , Song Jiangning 
Published: July 2024

AbstractThe accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (i) consistently outperforms currently-available predictors; (ii) provides accurate results when applied to inferred enzyme structures; and (iii) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental data sets, we show that SCREEN’s predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations.
Mechanical evolution of metastatic cancer cells in three-dimensional microenvironment
Authors: Hilai Karlin , Grubich Daniil , Akrawi Marcus , Zhu Hui , Zaghloul Razanne , Shi Chenjun , Do Man , Zhu Dongxiao , Zhang Jitao 
Published: July 2024

AbstractCellular biomechanics plays critical roles in cancer metastasis and tumor progression. Existing studies on cancer cell biomechanics are mostly conducted in flat 2D conditions, where cells’ behavior can differ considerably from those in 3D physiological environments. Despite great advances in developing 3Din vitromodels, probing cellular elasticity in 3D conditions remains a major challenge for existing technologies. In this work, we utilize optical Brillouin microscopy to longitudinally acquire mechanical images of growing cancerous spheroids over the period of eight days. The dense mechanical mapping from Brillouin microscopy enables us to extract spatially resolved and temporally evolving mechanical features that were previously inaccessible. Using an established machine learning algorithm, we demonstrate that incorporating these extracted mechanical features significantly improves the classification accuracy of cancer cells, from 74% to 95%. Building on this finding, we have developed a deep learning pipeline capable of accurately differentiating cancerous spheroids from normal ones solely using Brillouin images, suggesting the mechanical features of cancer cells could potentially serve as a new biomarker in cancer classification and detection.
Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders
Authors: Jovanovic Vukasin M. , Narisu Narisu , Bonnycastle Lori L. , Tharakan Ravi , Mesch Kendall T. , Glover Hannah J. , Yan Tingfen , Sinha Neelam , Sen Chaitali , Castellano David , Yang Shu , Blivis Dvir , Ryu Seungmi , Bennett Daniel F. , Rosales-Soto Giovanni , Inman Jason , Ormanoglu Pinar , LeClair Christopher A. , Xia Menghang , Schneider Martin , Hernandez-Ochoa Erick O. , Erdos Michael R. , Simeonov Anton , Chen Shuibing , Collins Francis S. , Doege Claudia A. , Tristan Carlos A. 
Published: July 2024

AbstractThe hypothalamus, composed of several nuclei, is essential for maintaining our body’s homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.
CD28 Shapes T Cell Receptor Signaling by Regulating ZAP70 Activation and Lck Dynamics
Authors: Raychaudhuri Kumarkrishna , Rangu Rohita , Ma Alison , Alvinez Neriah , Tran Andy D. , Pallikkuth Sandeep , McIntire Katherine M. , Garvey Joseph A. , Yi Jason , Samelson Lawrence E. 
Published: July 2024

AbstractT cell activation requires T cell receptor (TCR) engagement, which initiates a series of proximal events including tyrosine phosphorylation of the CD3 and TCRζ chains, recruitment, and activation of the protein tyrosine kinases Lck and ZAP70, followed by recruitment of adapter and signaling proteins. CD28 co-stimulation is also required to generate a functional immune response. Currently we lack a full understanding of the molecular mechanism of CD28 activation. TCR microclusters (MC) are submicron-sized molecular condensates and basic signaling units that form immediately after TCR ligation. Our results show that CD28 co-stimulation specifically accelerated recruitment of ZAP70 to the TCRζ chain in MCs and increased ZAP70 activation. This CD28-mediated acceleration of ZAP70 recruitment was driven by enhanced Lck recruitment to the MCs. A greater spatial separation between active and inactive species of Lck was also observed in the MCs as a consequence of CD28 co-stimulation. These results suggest that CD28 co-stimulation may lower the TCR activation threshold by enhancing the activated form of Lck in the TCR MCs.
Deep Learning-Based Classification of CRISPR Loci Using Repeat Sequences
Authors: Liao Xingyu , Li Yanyan , Wu Yingfu , Li Xingyi , Shang Xuequn 
Published: July 2024

AbstractWith the widespread application of the CRISPR-Cas system in gene editing and related fields, the demand for detecting and classifying CRISPR-Cas systems in metagenomic data has continuously increased. The traditional classification of the CRISPR-Cas system mainly relies on identifying neighboring cas genes of repeats. However, in some cases where there is a lack of information about cas genes, such as in metagenomes and fragmented genome assemblies, traditional classification methods may become ineffective. Here, we introduce a deep learning-based method called CRISPRclassify-CNN-Att, which classifies CRISPR-Cas systems solely based on repeat sequences. CRISPRclassify-CNN-Att utilizes convolutional neural networks (CNNs) and self-attention mechanisms to extract features from repeat sequences. It employs a stacking strategy to handle sample imbalances across different subtypes and improves classification accuracy for subtypes with fewer samples through transfer learning. CRISPRclassify-CNN-Att demonstrates excellent performance in classifying multiple subtypes, particularly in subtypes with a larger number of samples. Although CRISPR loci classification primarily relies on cas genes, CRISPRclassify-CNN-Att offers a new approach as a significant complement to current methods. It can identify unclassified loci missed by traditional cas-based methods, breaking the limitations of traditional approaches, and simplifying the classification process. The proposed tool is freely accessible viahttps://github.com/Xingyu-Liao/CRISPRclassify-CNN-Att.
Exploring the genetic and epigenetic underpinnings of early-onset cancers: Variant prioritization for long read whole genome sequencing from family cancer pedigrees
Authors: Kramer Melissa , Goodwin Sara , Wappel Robert , Borio Matilde , Offit Kenneth , Feldman Darren R. , Stadler Zsofia K. , McCombie W. Richard 
Published: July 2024

AbstractDespite significant advances in our understanding of genetic cancer susceptibility, known inherited cancer predisposition syndromes explain at most 20% of early-onset cancers. As early-onset cancer prevalence continues to increase, the need to assess previously inaccessible areas of the human genome, harnessing a trio or quad family-based architecture for variant filtration, may reveal further insights into cancer susceptibility. To assess a broader spectrum of variation than can be ascertained by multi-gene panel sequencing, or even whole genome sequencing with short reads, we employed long read whole genome sequencing using an Oxford Nanopore Technology (ONT) PromethION of 3 families containing an early-onset cancer proband using a trio or quad family architecture. Analysis included 2 early-onset colorectal cancer family trios and one quad consisting of two siblings with testicular cancer, all with unaffected parents. Structural variants (SVs), epigenetic profiles and single nucleotide variants (SNVs) were determined for each individual, and a filtering strategy was employed to refine and prioritize candidate variants based on the family architecture. The family architecture enabled us to focus on inapposite variants while filtering variants shared with the unaffected parents, significantly decreasing background variation that can hamper identification of potentially disease causing differences. Candidatedenovoand compound heterozygous variants were identified in this way. Gene expression, in matched neoplastic and pre-neoplastic lesions, was assessed for one trio. Our study demonstrates the feasibility of a streamlined analysis of genomic variants from long read ONT whole genome sequencing and a way to prioritize key variants for further evaluation of pathogenicity, while revealing what may be missing from panel based analyses.
GeneBag: training a cell foundation model for broad-spectrum cancer diagnosis and prognosis with bulk RNA-seq data
Authors: Liang Yuhu , Li Dan , Xu Aguix Guohua , Shao Yan , Tang Kun 
Published: July 2024

AbstractNumerous Pre-trained cell foundation models (CFM) have been crafted to encapsulate the comprehensive gene-gene interaction network within cells, leveraging extensive single-cell sequencing data. These models have shown promise in various cell biology applications, including cell type annotation, perturbation inference, and cell state embedding, etc. However, their clinical utility, particularly in cancer diagnosis and prognosis, remains an open question. We introduce the GeneBag model, a novel CFM that represents a cell as “a bag of unordered genes” with continuous expression values and a full-length gene list. Pre-trained on single-cell data and fine-tuned on bulk RNA-seq datasets, GeneBag achieves superior performance across cancer diagnosis and prognosis scenarios. In a zero-shot learning setting, GeneBag can classify cancer and non-cancer tissues with approximately 96.2% accuracy. With fine-tuning, it can annotate 40 different types of cancers and corresponding normal biopsies with an overall accuracy of ∼97.2%. It notably excels in classifying challenging cancers such as bladder (93%) and stomach (90%). Furthermore, GeneBag is capable of cancer staging with 68.5% accuracy and 5-year survival prediction with an AUC of ∼80.4%. This study marks the first to demonstrate the potential of CFMs in RNA-based cancer diagnostics and prognostics, indicating a promising avenue for AI-assisted molecular diagnosis.
Distributing Safer Smoking Pipes Increases Engagement with Harm Reduction Services in the United States: Findings from the National Survey of Syringe Services Programs
Authors: Chung Esther O. , Patel Sheila V. , Wenger Lynn D. , Humphrey Jamie L. , Bluthenthal Ricky N. , Tookes Hansel E. , Des Jarlais Don C. , Glick Sara N. , LaKosky Paul A. , Prohaska Stephanie , Guzman Laura , Kral Alex H. , Lambdin Barrot H. ,   National Survey of Syringe Services Programs Group
Published: July 2024

AbstractBackgroundIn response to the recent and growing shift from injecting opioids to smoking fentanyl, an increasing number of syringe services programs (SSPs) in the USA are distributing safer smoking supplies. A recent federal ban prevents SSPs from using federal funding to procure safer smoking supplies. There is a lack of research on safer smoking supply distribution and harm reduction outcomes. Therefore, we assessed the relationship between distribution of safer smoking supplies by SSPs and levels of participant engagement and naloxone distribution.MethodsWe used data from the 2023 National Survey of Syringe Services Programs (NSSSP) (N=429), which measured services delivered in 2022. SSPs reported whether they distributed safer smoking pipes (yes/no). We examined the relationship between safer smoking pipe distribution and two outcomes: the number of participant encounters and naloxone doses distributed.ResultsThere were 187 SSPs (43.6%) that distributed pipes for smoking to participants. Compared to SSPs that did not distribute pipes, those that distributed pipes reported more participant encounters (aRR=1.49, 95% CI: 1.09-2.02) and naloxone dose distribution (aRR=1.21, 95% CI: 0.89-1.65), though this latter finding was not statistically significant.ConclusionsFindings showed SSPs distributing safer smoking pipes had more participant engagement and naloxone distribution. To maximize their full individual and population-level health benefits, SSPs should be supported technically, legally, and financially to implement safer smoking supply distribution for their participants.HighlightsIn 2022, 44% of syringe services programs (SSPs) distributed safer smoking pipes.More community-based organizations distributed pipes than public health or health care programs.Distribution of safer smoking pipes was associated with more participant encounters.
APV9WR: An Integrated Web Resource of Alpha papillomavirus 9 for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis
Authors: Kulshreshtha Akanksha , Goel Vasu , Verma Akriti , Goel Sparsh , Dinesh Susha , Sharma Sameer , Bhowmik Ratul , Aspatwar Ashok 
Published: July 2024

ABSTRACTPurposeAlphapapillomavirus 9 is a virus belonging to the Papillomaviridae family. It has a close genetic relationship with high-risk HPV-16 and other HPV strains such as HPV-31, HPV-52, HPV-35, HPV-58, HPV-67, and HPV-35. This virus is responsible for causing warts and malignant tumors and is responsible for about 75% of cervical malignancies and pre-cancerous lesions worldwide. As a result, it requires specialized research and attention. Our goal is to create a comprehensive resource that can assist researchers and scientific groups in their work.Material and MethodologyA total of 1230 full genome sequences and 9140 protein sequences were obtained from GenBank and NCBI Virus, respectively. Further Phylogenetic Analysis, Codon usage and context analysis, CpG islands analysis, Glycosylation sites, Diagnostic Primers, B cell Epitopes and MHC I and MHC II binders were identified and analyzed using relevant Bioinformatics tools and Python programResultsAPV9WR is a web resource that was developed after analysis. Our data indicate that HPV 35 and HPV 38 have the most genomic diversity. From codon usage analysis, it was observed that AAA, AUU, UAU, UGU, and UUU are the most used codons, while ACG, CCG, CGA, CGG, CGU, GCG, and UCG are some of the unusual codons in APV9 nucleotide sequence with accession id -LC626346.1. We found 4714 CpG island locations in 1230 complete nucleotide sequences of APV9, and only 663 CpG island locations were unique. Further N-linked glycosylation, O-linked glycosylation, diagnostic primers, Potential B-cell epitopes and MHC I and MHC II binders were also analyzed and tabulated.ConclusionWe have consolidated basic information about the virus, such as entire genomic sequences and proteins. It primarily comprises a wide range of studies and outcomes, including genome alignment, phylogenetic inferences, codon context and usage bias, and important CpG island statistics. Furthermore, primers for molecular diagnostics were identified, and glycosylation sites were located and investigated. Most significantly, potential therapeutic elements such as vaccine epitopes and obtaining potential information about them were investigated. Our collective effort on this tool is meant to serve the greater good of the research community for therapeutic intervention for Alphapapillomavirus 9. Using this linkhttps://apv9nsut.web.appwill take you to the web app.