bluuebunny/crossref_metadata_2025_split huggingface dataset:
An active electronic, high-density epidural paddle array for chronic spinal cord neuromodulation
Authors: Parker Samuel R. , Calvert Jonathan S. , Darie Radu , Jang Jaeson , Govindarajan Lakshmi Narasimhan , Angelino Keith , Chitnis Girish , Iyassu Yohannes , Shaaya Elias , Fridley Jared S. , Serre Thomas , Borton David A. , McLaughlin Bryan L. 
Published: June 2024

AbstractObjectiveEpidural electrical stimulation (EES) has shown promise as both a clinical therapy and research tool for studying nervous system function. However, available clinical EES paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic delivery device, limiting the treatment area or density of epidural electrode arrays. We aimed to eliminate this burden using advanced on-paddle electronics.ApproachWe developed a smart EES paddle with a 60-electrode programmable array, addressable using an active electronic multiplexer embedded within the electrode paddle body. The electronics are sealed in novel, ultra-low profile hermetic packaging. We conducted extensive reliability testing on the novel array, including a battery of ISO 10993-1 biocompatibility tests and determination of the hermetic package leak rate. We then evaluated the EES device in vivo, placed on the epidural surface of the ovine lumbosacral spinal cord for 15 months.Main resultsThe active paddle array performed nominally when implanted in sheep for over 15 months and no device-related malfunctions were observed. The onboard multiplexer enabled bespoke electrode arrangements across, and within, experimental sessions. We identified stereotyped responses to stimulation in lower extremity musculature, and examined local field potential responses to EES using high-density recording bipoles. Finally, spatial electrode encoding enabled machine learning models to accurately perform EES parameter inference for unseen stimulation electrodes, reducing the need for extensive training data in future deep models.SignificanceWe report the development and chronic large animalin vivoevaluation of a high-density EES paddle array containing active electronics. Our results provide a foundation for more advanced computation and processing to be integrated directly into devices implanted at the neural interface, opening new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.
Structure-specific variation in per- and polyfluoroalkyl substances toxicity among genetically diverseCaenorhabditis elegansstrains
Authors: Leuthner Tess C. , Zhang Sharon , Kohrn Brendan F , Stapleton Heather M. , Baugh L. Ryan 
Published: June 2024

AbstractBackgroundThere are >14,500 structurally diverse per- and polyfluoroalkyl substances (PFAS). Despite knowledge that these “forever chemicals” are in 99% of humans, mechanisms of toxicity and adverse health effects are incompletely known. Furthermore, the contribution of genetic variation to PFAS susceptibility and health consequences is unknown.ObjectivesWe determined the toxicity of a structurally distinct set of PFAS in twelve genetically diverse strains of the genetic model systemCaenorhabditis elegans.MethodsDose-response curves for four perfluoroalkyl carboxylic acids (PFNA, PFOA, PFPeA, and PFBA), two perfluoroalkyl sulfonic acids (PFOS and PFBS), two perfluoroalkyl sulfonamides (PFOSA and PFBSA), two fluoroether carboxylic acids (GenX and PFMOAA), one fluoroether sulfonic acid (PFEESA), and two fluorotelomers (6:2 FCA and 6:2 FTS) were determined in theC. eleganslaboratory reference strain, N2, and eleven genetically diverse wild strains. Body length was quantified by image analysis at each dose after 48 hr of developmental exposure of L1 arrest-synchronized larvae to estimate effective concentration values (EC50).ResultsThere was a significant range in toxicity among PFAS: PFOSA > PFBSA ≈ PFOS ≈ PFNA > PFOA > GenX ≈ PFEESA > PFBS ≈ PFPeA ≈ PFBA. Long-chain PFAS had greater toxicity than short-chain, and fluorosulfonamides were more toxic than carboxylic and sulfonic acids. Genetic variation explained variation in susceptibility to PFBSA, PFOS, PFBA, PFOA, GenX, PFEESA, PFPeA, and PFBA. There was significant variation in toxicity amongC. elegansstrains due to chain length, functional group, and between legacy and emerging PFAS.ConclusionC. elegansrespond to legacy and emerging PFAS of diverse structures, and this depends on specific structures and genetic variation. Harnessing the natural genetic diversity ofC. elegansand the structural complexity of PFAS is a powerful New Approach Methodology (NAM) to investigate structure-activity relationships and mechanisms of toxicity which may inform regulation of other PFAS to improve human and environmental health.
The theory of massively repeated evolution and full identifications of Cancer Driving Nucleotides (CDNs)
Authors: Zhang Lingjie , Deng Tong , Liufu Zhongqi , Liu Xueyu , Chen Bingjie , Hu Zheng , Liu Chenli , Tracy Miles E. , Lu Xuemei , Wen Haijun , Wu Chung-I 
Published: June 2024

ABSTRACTTumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are Cancer Driving Nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size n = 300 - 1000), point mutations may recur iniout ofnpatients. This study explores a wide range of mutation characteristics to determine the limit of recurrences (i*) driven solely by neutral evolution. Since no neutral mutation can reachi*= 3, all mutations recurring ati≥ 3 are CDNs. The theory shows the feasibility of identifying almost all CDNs ifnincreases to 100,000 for each cancer type. At present, only < 10% of CDNs have been identified. When the full sets of CDNs are identified, the evolutionary mechanism of tumorigenesis in each case can be known and, importantly, gene targeted therapy will be far more effective in treatment and robust against drug resistance.
On the discovered Cancer Driving Nucleotides (CDNs)–Distributions across genes, cancer types and patients
Authors: Zhang Lingjie , Deng Tong , Liufu Zhongqi , Chen Xiangnyu , Wu Shijie , Liu Xueyu , Shi Changhao , Chen Bingjie , Hu Zheng , Cai Qichun , Liu Chenli , Li Mengfeng , Tracy Miles E. , Lu Xuemei , Wu Chung-I , Wen Haijun 
Published: June 2024

ABSTRACTA central goal of cancer genomics is to identify, in each patient, all the cancer driving mutations. Among them, point mutations are referred to as Cancer Driving Nucleotides (CDNs), which recur in cancers. The companion study shows that the probability ofirecurrent hits innpatients would decrease exponentially withi; hence, any mutation withi≥ 3 hits in the TCGA database is a high-probability CDN. This study characterizes the 50∼150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows:i) CDNs tend to code for amino acids of divergent chemical properties.ii) At the genic level, far more CDNs (>5-fold) fall on non-canonical than canonical cancer driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs.iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level.iv) Most important, among the 50∼100 coding region mutations carried by a cancer patient, 5∼8 CDNs are expected but only 0∼2 CDNs have been identified at present. This low level of identification has hampered functional test and gene targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.
Pre-TCR-Targeted Immunotherapy for T-cell Acute Lymphoblastic Leukemia
Authors: Fuentes Patricia , García-Peydró Marina , Mosquera Marta , Cela Carmela , Alcain Juan , Camós Mireia , Alarcón Balbino , Toribio María L. 
Published: June 2024

SUMMARYTargeted immunotherapy for T-cell acute lymphoblastic leukemia (T-ALL), an aggressive tumor of developing T-cell progenitors, is an urgent unmet need, especially for relapsed/refractory (r/r) disease. Selective T-ALL targeting is challenging due to the shared antigen expression between leukemic and normal T cells. Here we identify the pre-TCR, a surface receptor essential for T-cell development, as a biomarker of leukemia-initiating cells (LICs) in human T-ALL. Loss-of-function genetic approaches demonstrate that pre-TCR signaling is necessary for LIC activity and tumor progression in pre-TCR+T-ALL patient’s xenografts. Furthermore, we demonstrate the specific therapeutic targeting of pre-TCR with a monoclonal antibody against the invariant pTα subunit of the human pre-TCR, and validate an anti-pTα antibody-drug conjugate treatment as a potent immunotherapy for inhibiting LIC activity and tumor progression of T-ALLin vivo. These findings reveal the suitability of pre-TCR targeting as a promising therapy for the treatment of (r/r) patients with T-ALL expressing the pre-TCR.
Genome-wide conditional degron libraries for functional genomics
Authors: Gameiro Eduardo , Juárez-Núñez Karla A. , Fung Jia Jun , Shankar Susmitha , Luke Brian , Khmelinskii Anton 
Published: June 2024

AbstractThe yeast knockout library, comprised of strains carrying precise deletions of individual open reading frames (ORFs), has been a vital resource to understand gene function. However, accumulation of suppressor mutations in knockout strains can lead to erroneous functional annotations. Moreover, the library is limited to non-essential ORFs, and must be complemented with hypomorphic alleles of essential ORFs for genome- wide studies. To address these limitations, we constructed genome-wide libraries of conditional alleles based on the auxin-inducible degron (AID) system for conditional degradation of AID-tagged proteins. First, we determined that N-terminal tagging is at least twice more likely to inadvertently impair protein function across the proteome. We thus constructed two genome-wide libraries with over 5600 essential and non-essential proteins fused at the C-terminus with an AID tag and an optional fluorescent protein. Almost 90% of AID- tagged proteins were degraded in the presence of the auxin analog 5-Ph-IAA, with initial protein abundance and tag accessibility as limiting factors. Genome-wide screens for DNA damage response factors with the AID libraries revealed a role for the glucose signaling factorGSF2in resistance to hydroxyurea, highlighting how these resources extend the toolbox for functional genomics in yeast.
Ancient genomes reveal Avar-Hungarian transformations in the 9th-10th centuries CE Carpathian Basin
Authors: Gerber Dániel , Csáky Veronika , Szeifert Bea , Borbély Noémi , Jakab Kristóf , Mező György , Petkes Zsolt , Szücsi Frigyes , Évinger Sándor , Líbor Csilla , Rácz Piroska , Kiss Krisztián , Mende Balázs Gusztáv , Szőke Béla Miklós , Szécsényi-Nagy Anna 
Published: June 2024

AbstractDuring the Early Medieval period, the Carpathian Basin witnessed significant demographic shifts, notably under the Avar dominance for approximately 250 years, followed by the settlement of early Hungarians in the region during the late 9th century CE. This study presents the genetic analysis of 296 ancient samples, including 103 shotgun-sequenced genomes, from present-day Western Hungary. By employing identity-by-descent (IBD) segment sharing networks, this research offers detailed insights into the population structure and dynamics of the region from the 5th to the 11th centuries CE, with specific focus on certain microregions. Our evaluations reveal spatially different histories in Transdanubia even between communities in close geographical proximity, highlighting the importance of dense sampling and analyses. Our findings highlight extensive homogenisation and reorganization processes, discontinuities between Hun, Avar, and Conquest period immigrant groups, alongside the spread and integration of ancestry related to the Hungarian conquerors.TeaserGenetic formation of medieval Hungary, discontinuity between immigrant groups in the first millennium CE
TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore
Authors: Declercq Arthur , Devreese Robbe , Scheid Jonas , Jachmann Caroline , Den Bossche Tim Van , Preikschat Annica , Gomez-Zepeda David , Rijal Jeewan Babu , Hirschler Aurélie , Krieger Jonathan R , Srikumar Tharan , Rosenberger George , Trede Dennis , Carapito Christine , Tenzer Stefan , Walz Juliane S , Degroeve Sven , Bouwmeester Robbin , Martens Lennart , Gabriels Ralf 
Published: June 2024

AbstractThe high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields – such as plasma proteomics, immunopeptidomics, and metaproteomics – must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available athttps://github.com/compomics/tims2rescore.
FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit
Authors: Li James Wenhan , Bandaru Ravi , Liu Yaping 
Published: June 2024

AbstractCell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory-efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ∼100X cfDNA whole-genome sequencing (WGS) dataset with over 1 billion fragments in 1.2 hours, offering up to a ∼50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original approaches or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community:https://github.com/epifluidlab/FinaleToolkit
Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut
Authors: Schmitz Matthias , Dimonaco Nicholas J. , Clavel Thomas , Hitch Thomas C.A. 
Published: June 2024

AbstractMicrobes use a range of genetic codes and gene structures, yet these are ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes partial predictions, and optimises prediction of small proteins. Applied to 9,634 metagenomes and 3,594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, many with antimicrobial activity. Integration of the protein sequences and sample metadata into a tool, InvestiGUT, enables association of protein prevalence with host parameters. Accurate prediction of proteins is critical for understanding the functionality of microbiomes, hence this work will enhance understanding mechanistic interactions between microbes and hosts.
Systematic genetic characterization of the human PKR kinase domain highlights its functional malleability to escape a poxvirus substrate mimic
Authors: Chambers Michael J. , Scobell Sophia B. , Sadhu Meru J. 
Published: June 2024

ABSTRACTEvolutionary arms races can arise at the contact surfaces between host and viral proteins, producing dynamic spaces in which genetic variants are continually pursued. However, the sampling of genetic variation must be balanced with the need to maintain protein function. A striking case is given by protein kinase R (PKR), a member of the mammalian innate immune system. PKR detects viral replication within the host cell and halts protein synthesis to prevent viral replication by phosphorylating eIF2α, a component of the translation initiation machinery. PKR is targeted by many viral antagonists, including poxvirus pseudosubstrate antagonists that mimic the natural substrate, eIF2α, and inhibit PKR activity. Remarkably, PKR has several rapidly evolving residues at this interface, suggesting it is engaging in an evolutionary arms race, despite the surface’s critical role in phosphorylating eIF2α. To systematically explore the evolutionary opportunities available at this dynamic interface, we generated and characterized a library of 426 SNP-accessible nonsynonymous variants of human PKR for their ability to escape inhibition by the model pseudosubstrate inhibitor K3, encoded by the vaccinia virus geneK3L. We identified key sites in the PKR kinase domain that harbor K3-resistant variants, as well as critical sites where variation leads to loss of function. We find K3-resistant variants are readily available throughout the interface and are enriched at sites under positive selection. Moreover, variants beneficial against K3 were also beneficial against an enhanced variant of K3, indicating resilience to viral adaptation. Overall, we find that the eIF2α-binding surface of PKR is highly malleable, potentiating its evolutionary ability to combat viral inhibition.
Multi-task benchmarking of spatially resolved gene expression simulation models
Authors: Liang Xiaoqi , Cao Yue , Hwa Yang Jean Yee 
Published: June 2024

AbstractComputational methods for spatially resolved transcriptomics (SRT) are frequently developed and assessed through data simulation. The effectiveness of these evaluations relies on the simulation methods’ ability to accurately reflect experimental data. However, a systematic evaluation framework for spatial simulators is lacking. Here, we present SpatialSimBench, a comprehensive evaluation framework that assesses 13 simulation methods using 10 distinct STR datasets. We introduce simAdaptor, a tool that extends single-cell simulators to incorporate spatial variables, thus enabling them to simulate spatial data. SimAdaptor enables SpatialSimBench to be “back-wards” compatible. That is, it facilitates direct comparison between spatially aware simulators and existing non-spatial single-cell simulators through the adaption. Through SpatialSimBench, we demonstrate the feasibility of leveraging existing single-cell simulators for SRT data and highlight performance differences among methods. Additionally, we evaluate the simulation methods based on a total of 35 metrics across data property estimation, various downstream analysis and scalability. In total, we generated 4550 results from 13 simulation methods, 10 spatial datasets and 35 metrics. Our findings reveal that model estimation can be impacted by distribution assumptions and dataset characteristics. In summary, our evaluation and the evaluation framework will provide guidelines for selecting appropriate methods for specific scenarios and informing future method development.
Toxicity of nitrification inhibitors to soil-living animals
Authors: Bruus Marianne , Krogh Paul Henning 
Published: June 2024

AbstractThe toxicity of four substances appearing in nitrification inhibitors (DMPP, nitrapyrin, triazole and methylpyrazole) was tested towards three selected soil fauna species, i.e. the springtailFolsomia candida, the enchytraeidEnchytraeus albidusand the earthwormEisenia fetida. Range-finding and definitive tests were performed according to OECD standard protocols. Test results were compared to expected exposure in order to assess the risk of using nitrification inhibitors. Especially nitrapyrin was estimated to pose a potential risk to soil fauna.
Extracellular vesicle mobility in collagen I hydrogels is influenced by RGD-binding integrins
Authors: Tam Nicky W. , Becker Alexander , Mangiarotti Agustín , Cipitria Amaia , Dimova Rumiana 
Published: June 2024

AbstractExtracellular vesicles (EVs) are a diverse population of membrane structures produced and released by cells into the extracellular space for the intercellular trafficking of cargo molecules. They are implicated in various biological processes, including angiogenesis, immunomodulation, and cancer cell signaling. While much research has focused on their biogenesis or their effects on recipient cells, less is understood about how EVs are capable of traversing diverse tissue environments and crossing biological barriers. Their interactions with extracellular matrix components are of particular interest, as such interactions govern diffusivity and mobility, providing a potential basis for organotropism. To start to untangle how EV-matrix interactions affect diffusivity, we use highspeed epifluorescence microscopy, single particle tracking, and confocal reflectance microscopy to analyze particle mobility and localization in extracellular matrix-mimicking hydrogels composed of collagen I. EVs are compared with synthetic liposomes and extruded plasma membrane vesicles to better understand the importance of membrane composition on these interactions. By treating EVs with trypsin to digest surface proteins, we determine that proteins are primarily responsible for EV immobilization in collagen I hydrogels. We next use a synthetic peptide competitive inhibitor to narrow down the identity of the proteins involved to argynylglycylaspartic acid (RGD) motif-binding integrins, which interact with unincorporated or denatured non-fibrillar collagen. Moreover, the effect of integrin inhibition with RGD peptides has strong implications for the use of RGD-peptide-based drugs to treat certain cancers, as integrin inhibition appears to increase EV mobility, improving their ability to infiltrate tissue-like environments.