bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of cladribine on intrathecal and peripheral B and plasma cells
Authors: Allen-Philbey Kimberley , Stephenson Sophie , Doody Gina , MacDougall Amy , Aboulwafaali Mohammad , Ammoscato Francesca , Andrews Michael , Gnanapavan Sharmilee , Giovannoni Gavin , Grigoriadou Sofia , Hickey Alaco , Holden David W. , Lock Helen , Papachatzaki Maria , Redha Iman , Baker David , Tooze Reuben , Schmierer Klaus 
Published: September 2024

ABSTRACTBackgroundCladribine is a deoxyadenosine analogue that can penetrate the blood-brain barrier. It is used to treat multiple sclerosis. However, the mechanistic understanding of the effect of this highly effective therapy on B cells and plasma cells in the central nervous system compartment is limited.ObjectivesThe CLADRIPLAS study examined the effect of cladribine on peripheral and intrathecal B and plasma cell biology in people with MS.MethodsThirty-eight people with progressive MS ineligible for- or rejecting-treatment with licenced therapies were recruited and supplied a baseline lumbar puncture. Those exhibiting gadolinium-enhancing or new/enlarging T2 magnetic resonance imaging lesions and/or elevated neurofilament levels were offered subcutaneous cladribine (Litak®). Seven people were eligible; one person died before treatment, and only five completed the first year of treatment. Twenty-two ineligible people were willing to provide a repeat lumbar puncture twelve months later.ResultsThe CLADRIPLAS study found no evidence of a difference in the odds of a positive cerebrospinal fluid oligoclonal band (cOCB) result between the cladribine-treated and untreated group. This is probably explained by microarray andin vitrostudies, which demonstrated that plasmablasts and notably long-lived plasma cells are relatively resistant to the cytotoxic effect of cladribine compared to memory B cells at physiological concentrations. This was consistent with the loss of intracellular deoxycytidine kinase during antibody-secreting cell differentiation.DiscussionCLADRIPLAS indicates that cOCB are not rapidly eliminated in most people with MS. This may be explained by the relative lack of direct cytotoxic action of cladribine on long-lived plasma cells.
SMART MRS: A Simulated MEGA-PRESS Artifacts Toolbox for GABA-edited MRS
Authors: Bugler Hanna , Shamaei Amirmohammad , Souza Roberto , Harris Ashley D. 
Published: September 2024

AbstractPurposeTo create a Python-based toolbox to simulate commonly occurring artifacts for single voxel Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) data.MethodsThe toolbox was designed to maximize user flexibility and contains artifact, applied, input/output (I/O), and support functions. The artifact functions can produce spurious echoes, eddy currents, nuisance peaks, line broadening, baseline contamination, linear frequency drifts, and frequency and phase shift artifacts. Applied functions combine or apply specific parameter values to produce recognizable effects such as lipid peak and motion contamination. I/O and support functions provide additional functionality to accommodate different kinds of input data (MATLAB FID-A .mat files, nifti-mrs files), which vary by domain (time vs. frequency), collection type (edited vs. non-edited) and scale.ResultsUsers may provide some or all of the required artifact function parameter values which will result in artifacts of different appearances. Visual assessment confirms the resemblance of simulated compared toin vivoproduced artifacts.ConclusionOur pip installable Python artifact simulated toolboxSMART_MRSis meant to enhance the diversity and quality of existing simulated edited-MRS data and is complementary to existing MRS simulation software.
Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives
Authors: Müller Jason M. , Altendorfer Elisabeth , Freier Susanne , Moos Katharina , Mayer Andreas , Tresch Achim 
Published: September 2024

AbstractWe introduce Halfpipe, a tool for analyzing RNA-seq data from metabolic RNA labeling experiments. Its main features are the absolute quantification of 4sU-labeling-induced T>C conversions in the data as generated by SLAM-seq, calculating the proportion of newly synthesized transcripts, and estimating subcellular RNA half-lives. Halfpipe excels at correcting critical biases caused by typically low labeling efficiency. We measure and compare the RNA metabolism in the G1 phase and during the mitosis of synchronized human cells. We find that RNA half-lives of constantly expressed RNAs are similar in mitosis and G1 phase, suggesting that RNA stability of those genes is constant throughout the cell cycle. Our estimates correlate well with literature values and with known RNA sequence features. Halfpipe is freely available athttps://github.com/IMSBCompBio/Halfpipe
The linear correlation between genome size and the size of the non-transcribing region
Authors: Zhang-Ren Chen 
Published: September 2024

AbstractBackgroundThe genome sizes of organisms vary widely (C-value paradox). There are non-transcribing regions in the genome that neither encode proteins nor RNA entities. There are several hypotheses about the function of these regions: one suggests that they are unannotated functional areas, while another views them as genomic isolation zones that reduce mutations in coding regions.MethodStatistical analysis was conducted on the transcribing regions (including areas annotated as genes and transcribed pseudogenes) and non-transcribing regions, protein-coding regions (Coding sequence, CDS), and genome sizes using annotation files from 63,866 species genomes in the NCBI RefSeq database.ResultsThere is a significant linear relationship between the size of non-transcribing genomic regions and overall genome size across species, with varying proportional coefficients among different phyla (realms for viruses). As genome size increases, the proportion of non-transcribing regions gradually rises, eventually approaching a linear proportional limit, resembling one arm of hyperbolic functions. Eukaryotes show high linear correlation, with the highest in Streptophyta and the lowest in Apicomplexa. In eukaryotes, the size of the coding region increases with genome size, but the increasing trend diminishes (proportionally decreases). In non-eukaryotes, the size of the coding region maintains a linear relationship with genome size.ConclusionThe size of non-transcribing region in species may be subject to some strict quantitative control mechanism, showing that genome and non-transcribing genome sizes increase proportionally with the expansion of the transcribing genome, indicating a strict balance between expansion and energy conservation. The proportion of non-transcribed genomes in eukaryotes is conservative (although the sequences are not), and the presence of non-transcribing genomes has significant implications for the evolution or survival of species. Thus, I propose a new hypothesis about the non-transcribing genome, that it is a space for generating new genes from scratch, and the different proportional coefficients among phyla are due to their different positions in energy transfer.Graphic Abstract
A lymphoid tissue chemokine checkpoint prevents loss of CD8+T cell functionality
Authors: Altenburger Lukas M. , Claudino Carvoeiro Daniela , Dehio Philippe , Zhou Jianwen , Laura Chiara , Katoch Mitali , Krüger Caroline , Barreto de Albuquerque Juliana , Pfenninger Petra , Martínez Magdaleno Jose , Abe Jun , Mehling Matthias , Dengjel Jörn , Iannacone Matteo , Gheinani Ali Hashemi , Stein Jens V. 
Published: September 2024

AbstractThe generation of effector CD8+T cells (TEFF) requires activation of naive CD8+T cells (TN) by dendritic cells (DCs) within lymphoid tissue. To date, it remains elusive how the duration of TN-DC interactions and integration of activation signals are controlledin vivo. Here, we report that lymphoid stroma-secreted ligands for CCR7 constrained interaction duration by gradually inducing CD8+T cell release from DCs. At late time points of interactions, CCR7 ligands repositioned the F-actin-promoting factor DOCK2 away from the DC interface to enable CD8+T cell detachment, proliferation onset and acquisition of cytotoxicity. Lack of CCR7 signaling, as duringex vivoactivation or in chronically inflamed lymphoid tissue, caused sustained T cell-DC interactions, and generated dysfunctional TEFFwith high expression of inhibitory receptors, impaired antimicrobial activity, and poor recall responses. In sum, our findings uncover that lymphoid stromal chemokines act as built-in “disruptors” of T cell-DC interactions for long-term preservation of TEFFfunctionality.
Multi-generational biotic stress increases the rate of spontaneous epimutations in a ROS1-dependent manner
Authors: Zhang Zhilin , Parker Adam Hannan , Symeonidi Aikaterini , Wilkinson Samuel W. , Stassen Joost H. M. , Smith Lisa M. , Ton Jurriaan , Johannes Frank 
Published: September 2024

AbstractMistakes in the maintenance of CG methylation are a source of spontaneous epimutations in plants that can be inherited across generations. The extent to which these stochastic events are affected by prolonged exposure to biotic and abiotic stress remains poorly characterized. Here, we grew Arabidopsis Mutation Accumulation (MA) lines for 12 generations in the presence of two biotic stressors:Pseudomonas syringae(Pst) and salicylic acid (SA). We found that multi-generational exposure to Pst and SA led to an 18%-32% and 23%-61% increase in the genome-wide epimutation rate, respectively. These rate increases were mainly targeted to subsets of genes characterized by low steady-state methylation (LM), on average, and a lack of transcriptional responsiveness to stress. We show that these effects are mediated by the DNA demethylaseRepressor of Silencing 1(ROS1). Loss of ROS1 not only buffers transcriptional responses to biotic stress, but also stabilizes epimutation rates, particularly in LM genes, rendering them insensitive to environmental perturbations. Taken together, our data demonstrates that stress can induce heritable epimutations and highlights a ROS1-mediated link between transcriptional plasticity and DNA methylation maintenance fidelity over generations.
CanDrivR-CS: A Cancer-Specific Machine Learning Framework for Distinguishing Recurrent and Rare Variants
Authors: Francis Amy , Campbell Colin , Gaunt Tom 
Published: September 2024

AbstractMotivationMissense variants play a crucial role in cancer development, and distinguishing between those that frequently occur in cancer genomes and those that are rare may provide valuable insights into important functional mechanisms and consequences. Specifically, if common variants confer growth advantages, they may have undergone positive selection across different patients due to similar selection pressures. Moreover, studies have demonstrated the significance of rare mutations that arise as resistance mechanisms in response to drug treatment. This highlights the importance of understanding the role of both recurrent and rare variants in cancer. In addition to this, most existing tools for variant prediction focus on distinguishing variants found in normal and disease populations, often without considering the specific disease contexts in which these variants arise. Instead, they typically build predictors that generalise across all diseases. Here, we introduceCanDrivR-CS, a set of cancer-specific gradient boosting models designed to distinguish between rare and recurrent cancer variants.ResultsWe curated missense variant data from the International Cancer Genome Consortium (ICGC). Cancer-type-specific models significantly outperformed a baseline pan-cancer model, achieving a maximum leave-one-group-out cross-validation (LOGO-CV) F1 score of up to 90% forCanDrivRSKCM (Skin Cutaneous Melanoma)and 89% forCanDrivR-SKCA (Skin Adenocarcinoma), compared to 79.2% for the baseline model. Notably, DNA shape properties consistently ranked among the top features for distinguishing recurrent and rare variants across all cancers. Specifically, recurrent missense variants frequently occurred in DNA bends and rolls, potentially implicating regions prone to DNA replication errors and acting as mutational hotspots.Availability and ImplementationAll training and test data, and Python code are available in ourCanDrivR-CSGitHub repository:https://github.com/amyfrancis97/CanDrivR-CS.
Discovering Governing Equations of Biological Systems through Representation Learning and Sparse Model Discovery
Authors: Sadria Mehrshad , Swaroop Vasu 
Published: September 2024

AbstractUnderstanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional embeddings and uncovers the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological datasets, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERA’s ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.
Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data
Authors: Benavides Tiburon L. , Montelione Gaetano T. 
Published: September 2024

ABSTRACTProtein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we presentCSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection.CSP_Rankis a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.
Single-Session Feasibility of a Wearable Music-based Rhythmic Auditory Stimulation Device to Improve Walking in Knee Osteoarthritis
Authors: Yang Yiwen , Okamura Michele , Kim Ehyun , Kumar Deepak 
Published: September 2024

ABSTRACTObjectiveWe examined the single-session feasibility of a wearable device to deliver a closed-loop music-based rhythmic auditory stimulation (RAS) walking intervention in people with knee osteoarthritis (OA).MethodsFifteen participants with knee OA (age 70.0 ± 8.6 years; BMI 29.4±5.9 kg/m2; 10 women) complete a supervised 30-minute walking session using a wearable music-based RAS device. The device included shoe-worn inertial sensors, headphones, and a proprietary algorithm embedded in a touchscreen control unit. The algorithm continuously assessed users’ gait and adjusted the tempo of the selected music to encourage improvements in gait quality. Spatiotemporal gait measures were collected using separate inertial sensors during the session. We examined change in gait during the session, participant’s report of exertion, and participant-reported acceptability.ResultsOn average, participants showed ∼12%, 30%, and 15% increases in cadence, gait speed, and stride length, respectively without worsening variability or symmetry from before to during the RAS session. Of the 15 participants, 9/15, 11/15, and 9/15 showed ≥ 10% increase in cadence, gait speed, and stride length, respectively. Rating of Perceived Exertion was on average 13.3 (range 12-15). There were no serious adverse events and small increase in knee pain that resolved at the end of the session. Participants were highly accepting of the device and willing to use it at home for longer periods.ConclusionA single-session feasibility study of a wearable closed-loop music-based RAS device in people with knee OA demonstrated increases in cadence and gait speed while safely walking at a moderate exercise intensity under supervision.
A quantitative approach to evidence triangulation: development of a framework to address rigour and relevance
Authors: Shapland Chin Yang , Bell Joshua A. , Borges Maria-Carolina , Goncalves Soares Ana , Davey Smith George , Gaunt Tom R. , Lawlor Deborah A. , McGuinness Luke A. , Tilling Kate , Higgins Julian P.T. 
Published: September 2024

AbstractTriangulation is an approach to strengthening causal inference by integrating evidence from multiple sources. Most studies using triangulation have qualitatively examined whether different studies agree upon the presence of a causal effect, rather than estimated the effect by quantitatively integrating results. Here, we develop a framework for quantitative triangulation. We first address how to relate study specific research questions to an overall target causal question (relevance), and then assess the directions and magnitudes of bias in each study (rigour), before combining the results using meta-analysis adjusted for the biases.We illustrate our framework by triangulating evidence from randomized controlled trials (RCTs), Mendelian randomization (MR) and conventional multivariable regression (MVR) to estimate the effect of beta-carotene on coronary heart disease (CHD) and cardiovascular disease (CVD). Five RCTs and one MR study showed little evidence of a causal relationship between beta-carotene and CHD (relative risk (RR)=1.00 with 95% CI=0.98 to 1.01 and RR=1.02 with 95% CI=0.98 to 1.07, respectively). 13 MVR studies indicated that high intake of beta-carotene reduces CHD risk (RR=0.83 with 95% CI0.76 to 0.91). After applying our framework, the three study designs agreed that there is little evidence of an effect of beta-carotene intake on the risk of CHD (RR=1.01 with 95% CI=0.99 to 1.02). Findings were similar for CVD.Our framework shows how to address rigour and relevance quantitatively when triangulating evidence from different study designs. We highlight the importance of explicitly defining the target and study-specific research questions.
Plasma proteomics reveals the potential causal impact of extracellular matrix proteins on abdominal aortic aneurysm
Authors: Khodursky Samuel , Yuan Shuai , Spin Joshua M. , Tsao Philip S. , Levin Michael G. , Damrauer Scott M. 
Published: September 2024

AbstractBackgroundAbdominal aortic aneurysm (AAA) is a common and life-threatening vascular disease. Genetic studies have identified numerous associated loci, many potentially encoding plasma proteins. However, the causal effects of plasma proteins on AAA have not been thoroughly studied. We used genetic causal inference approaches to identify plasma proteins that have a potential causal impact on AAA.MethodsCausal inference was performed using two-sample Mendelian randomization (MR). For AAA, we utilized recently published summary statistics from a multi-population genome-wide association (GWAS) meta-analysis including 39,221 individuals with, and 1,086,107 individuals without AAA from 14 cohorts. We used protein quantitative trait loci (pQTLs) identified in two large-scale plasma-proteomics studies (deCODE and UKB-PPP) to generate genetic instruments. We tested 2,783 plasma proteins for possible causal effects on AAA using two-sample MR with inverse variance weighting and common sensitivity analyses to evaluate the MR assumptions. Bayesian colocalization and gene ontology (GO) enrichment analyses provided additional insights.ResultsMR identified 90 plasma proteins associated with AAA at FDR<0.05, with 25 supported by colocalization analysis. Among those supported by both MR and colocalization were previously experimentally validated proteins such as PCSK9 (OR 1.3; 95%CI 1.2-1.4; P<1e-10), LTBP4 (OR 3.4; 95%CI 2.6-4.6; P<1e-10) and COL6A3 (OR 0.6; 95%CI 0.5-0.7; P<1e-6). GO analysis revealed enrichment of proteins found in extracellular matrix (ECM, OR 7.8; P<1e-4), some with maximal mRNA levels in aortic tissue. Bi-directional MR suggested plasma level changes were not caused by liability to AAA itself. We then investigated whether variants responsible for expression changes in the aorta also influenced plasma levels and AAA risk. Colocalization analysis showed that an aortic expression quantitative trait locus (eQTL) for COL6A3, and a splicing quantitative trait locus (sQTL) for LTBP4 colocalized with their respective plasma pQTLs and AAA signals (posterior probabilities 0.84 and 0.89, respectively).ConclusionsOur results highlight proteins and pathways with potential causal effects on AAA, providing a foundation for future functional experiments. These findings suggest a possible causal pathway whereby genetic variation affecting ECM proteins expressed in the aortic wall cause their levels to change in blood plasma, influencing development of AAA.
Linguistic Dynamics: Women vs. the General Population in Reddit’s ADHD Discussions
Authors: Rahman Muhammad Mahbubur 
Published: September 2024

AbstractUnderstanding how language reflects the experiences of individuals with attention-deficit/hyperactivity disorder (ADHD) is important for developing targeted support strategies. This research investigates the linguistic patterns exhibited in discussions within women-centric and general ADHD population on Reddit, exploring how gender influences language use. By analyzing language, the study uncovers unique linguistic patterns and illuminates how women with ADHD express themselves, share experiences, and seek support compared to the general ADHD population. By leveraging deep learning-based embedding, clustering and named entity recognition, the study conducts a comprehensive analysis. Results reveal that women with ADHD emphasize themes like “ADHD Awareness and Diagnosis” and “Emotional Well-being”. Conversely, the general population highlights “Medication and Treatment”, and “Work and Academic Challenges”. Statistical tests confirm significant differences in both linguistics and emotional expression between the two groups, emphasizing the importance of gender considerations in understanding ADHD language nuances and tailoring interventions accordingly.