bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolomics characterisation of cassava pre-breeding populations with enhanced whitefly tolerance
Authors: Perez-Fons L , Bohorquez-Chaux A , Gomez-Jimenez MI , Becerra Lopez-Lavalle LA , Fraser PD 
Published: February 2025

ABSTRACTCassava (Manihot esculentaCrantz) provides food security for over 500 million people in Sub-Saharan Africa (SSA). Whitefly (Bemisia tabaci) is a pest in this region that result in ca. 50% crop yield losses. Thus, it is important to develop approaches that will generate new varieties tolerant to this pest to advance food security in the region. Two parental cassava varieties, ECU72 tolerant to whiteflies and COL2246 a susceptible line have been used to generate bi-parental populations. The F1 generation has been screened for whitefly resistance and progeny identified displaying enhanced tolerance. From designated F1 tolerant progeny, F2 families have been generated and phenotyped. The tolerance to whiteflies in the F2 population was further enhanced. Untargeted metabolomics was used to characterise whitefly susceptible and tolerant sub-groups. PCA of the molecular features generated clustering of accessions into whitefly resistant and susceptible groups and differentiating metabolite biomarkers were identified. The most significant metabolite marker for resistance being the chemical feature 316.0924. Although not consistent among all whitefly resistance sub-groups targeted LC-MS analysis revealed several pathways displaying perturbed levels. These include cyanogenic glycosides, apocarotenoids and phenylpropanoid super-pathway comprising of hydroxycinnamic acids, flavonoids and proanthocyanidins. Thus, the generation of a bi-parental population for whitefly tolerance/susceptibility enabled the identification of quantitative metabolite markers, the pathways contributing to tolerance, the underlying modes of action associated with resistance and the potential for the development of future high-throughput low-cost proxy markers. The approach also provides generic insights into future breeding strategies utilising bi-parental progeny for the enhancement of traits.SUMMARY.SIGNIFICANCE STATEMENT.
A simple network of proprioceptive reflexes can produce a variety of bipedal gaits
Authors: Bunz Elsa K. , Haeufle Daniel F. B. , Schmitt Syn , Geijtenbeek Thomas 
Published: February 2025

AbstractProprioception is crucial for movement, yet the role of proprioceptive reflexes in legged locomotion is still poorly understood. While previous simulation studies have shown great potential for reflex-based control strategies, these controllers are typically catered to specific gaits, using hand-crafted feedback pathways that are linked to specific gait phases. In this work, we explore the control capabilities of a simple reflex controller that consists of only monosynaptic and antagonistic length and force feedback pathways with constant gains. Despite its simplicity, we found our control framework capable of producing a wide variety of natural gaits, including walking and hopping, forwards and backwards, and running in different variations and at different velocities – without requiring any rhythmic inputs or high-level state machines modulating the feedback gains. Our work highlights the important role and flexibility of proprioceptive reflexes and suggests a necessary re-evaluation of their role in locomotion. Due to its simplicity and flexibility, our control framework provides a solid basis for the development of higher level neuromuscular control systems.Author summaryHuman locomotion is characterized by an impressive versatility and humans transition naturally between different gaits. However, the neural mechanisms underlying human motor control are not fully understood and remain controversial. The two widely accepted basic components of the neural circuitry involved in locomotion are central pattern generators (CPG) and spinal reflexes, leading to a long-standing debate on their respective roles. Neuromusculoskeletal models can be employed to study the role of these neural control structures in simulation. Until now, these models mostly focus on walking and require an additional component like a state-machine or CPG besides reflexes to generate rhythmic activation of muscles. In this work, we propose a controller that can generate different rhythmic gaits like walking and hopping, forwards and backwards, as well as running, based solely on simple reflexes known to exist in the mammalian spinal cord. We challenge the current view of the role of rhythm-generating components and show the remarkable potential of reflexes for locomotion control. To our knowledge, this is the first work showing that versatile locomotion can be achieved solely based on reflexes without the need of a CPG. Our results contribute to the understanding of human motor control and are relevant for the control of robots, exoskeletons, prostheses and ortheses.
BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture
Authors: McDonald Thomas O. , Bruno Simone , Roney James P. , Zervantonakis Ioannis K. , Michor Franziska 
Published: February 2025

AbstractUnderstanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developedBayesian Estimation of STochastic processes for Dose-Response(BESTDR), a novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR quantifies rates in multi-state systems across multiple cell lines using hierarchical modeling to support high-throughput screening. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. BESTDR offers a versatile tool for early-stage preclinical research, paving the way for drug discovery and informed experimental design.
EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment
Authors: Wu Juncheng , Wan Changxin , Ji Zhicheng , Zhou Yuyin , Hou Wenpin 
Published: February 2025

AbstractFoundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individual cells. The principal challenge in developing such a model lies in the vast number of scATAC peaks and the significant sparsity of the data, which complicates the formulation of peak-to-peak correlations. To address this challenge, we introduceEpiFoundation, a foundation model for learning cell representations from the high-dimensional and sparse space of peaks. Epi-Foundation relies on an innovative cross-modality pre-training procedure with two key technical innovations. First, EpiFoundation exclusively processes the non-zero peak set, thereby enhancing the density of cell-specific information within the input data. Second, EpiFoundation utilizes dense gene expression information to supervise the pre-training process, aligning peak-to-gene correlations. EpiFoundation can handle various types of downstream tasks, including cell-type annotation, batch correction, and gene expression prediction. To train and validate EpiFoundation, we curatedMiniAtlas, a dataset of 100,000+ single cells with paired scRNA-seq and scATAC-seq data, along with diverse test sets spanning various tissues and cell types for robust evaluation. EpiFoundation demonstrates state-of-the-art performance across multiple tissues and diverse downstream tasks.
Genome-Wide Uncertainty-Moderated Extraction of Signal Annotations from Multi-Sample Functional Genomics Data
Authors: Hamilton Nolan H. , McMichael Benjamin D. , Love Michael I. , Furey Terrence S. 
Published: February 2025

AbstractMotivationMost genome-wide consensus profiles of regulatory features consist of discrete binary annotations, or “peaks”, identified as enriched in functional genomics data targeting loci with a particular molecular phenotype, e.g., accessible chromatin or a transcription factor binding site. Despite the established practical utility of this paradigm, these annotations can limit the representation of quantitative signal data to rigid boundaries and mask informative spatial and structural features in signals. We reasoned that a more robust integration of numeric data from multiple samples could elucidate higher-resolution genome-wide profiles for improved molecular state characterizations. This integration poses several fundamental challenges due to dynamic, sample-specific noise patterns that vary in their effects across the genome and render straightforward data aggregation approaches vulnerable to signal dilution or distortion.ResultsWe propose Consenrich, a genome-wide state-estimation scheme for extraction of consistent underlying signals given functional sequencing data from multiple samples. Consenrich addresses fundamental challenges inherent in this task by implementing a biologically-informed model for signal and variance propagation that treats nonstationary noise processes to yield a genome-wide signal track of positional estimates and measures of uncertainty. We demonstrate improved multi-sample signal estimation and noise attenuation of Consenrich on real, heterogeneous functional genomics data compared to least-squares smoothing filters and straightforward “alignment merging” strategies. We highlight immediate practical appeal of this improved signal extraction performance for both traditional consensus peak calling tasks and address the potential for unique analyses not possible with simple binary peak calls.Availability and ImplementationA software implementation of Consenrich accompanied by several practical usage examples and documentation is hosted athttps://github.com/nolan-h-hamilton/Consenrich.
Leveraging spatial scale and temporal variation to optimize estimates of invasive spread rates
Authors: Keller Joseph , De Bona Sebastiano , Helmus Matthew R. 
Published: February 2025

AbstractEstimating the extent and speed of an invasive species’ spread is crucial for optimizing surveys and management, but this estimation is challenging due to the scale-dependent nature of spread. For instance, when discretizing occurrence data to grids to estimate the extent of invaded ranges, larger cells can decrease boundary precision, while smaller cells can increase the risk of missing invaded areas. Moreover, on shorter timescales such as year-to-year comparisons, spread rates can exhibit lags, accelerations, and slowdowns. We present a multiscale spread optimization methodology developed for the spotted lanternfly (Lycorma delicatula), a pest impacting grapes that is spreading westward across the continental United States. We analyzed a dataset of >900,000 occurrence records covering spread from this pest’s initial detection in eastern Pennsylvania in 2014 to its invasion front in Chicago, IL, in 2023. First, we delineated the annual U.S. invasion front using square grids with varying cell sizes and α-convex hulls with different boundary resolutions. For each method and scale, we regressed invaded range area against year using both simple linear and logistic nonlinear models. Coarser spatial scales yielded faster estimated spread rates, and logistic models outperformed linear regressions, indicating a lag, acceleration, and slowdown in annual spread rate. Next, to determine the spatial scale that best captured the invasion front boundary, we used cross-validation, optimizing the Fβmetric, which balances recall and precision. Emphasizing recall generally favored coarser spatial scales, and α- convex hulls outperformed grid-based methods in delineating invasion fronts. Finally,L. delicatulahas dispersed long distances from its primary epicenter, establishing satellite populations. We delineated each satellite population each year since its inception using optimized α-hull values and measured their year-to-year boundary expansion. Like the primary epicenter, these satellite populations showed accelerating expansion, at least until merging with the primary invasion. Overall, the expansion rate of the primary invasion has slowed since 2017, decreasing from a maximum of 21 km/year, possibly due in part to control efforts, although the exact cause remains unresolved. Considering spatial scale and temporal variation can optimize the analysis of invasive spread and motivate early interventions to manage nascent epicenters before their spread accelerates.
Beyond Proxies: Towards ecophysiological indicators of drought resistance for forest management
Authors: Copie Alice , Scotti-Saintagne Caroline , Lefèvre François , Cochard Hervé , Delzon Sylvain , Dumas Pierre-Jean , Gounelle Damien , Herbette Stephane , Jean Florence , Mariotte Nicolas , Mencuccini Maurizio , Scotti Ivan , Martin-StPaul Nicolas 
Published: February 2025

AbstractAs drought-induced mortality increases globally in forest biomes, it becomes necessary for foresters to have access to reliable predictors of species vulnerability to drought and mortality risk under different climatic scenarios. On the one hand, there exist several “operational” indicators of drought resistance, which are based on observations, expert knowledge and species bioclimate (e.g.Ellenberg, Rameau, ClimEssences). On the other hand, as traits can now be measured at high throughput, functional traits (such as plant hydraulic traits) have been increasingly used to assess species’ vulnerability to hydraulic failure, a key process of tree mortality under drought. However, this mechanistic approach has never been compared to the operational approach. In this study, we review if indicators commonly used by foresters provide information onAbiesspecies’ vulnerability to hydraulic failure. We measured a set of traits in a common garden experiment of closely related MediterraneanAbiesspecies. These traits were used as inputs to the SurEau model to compute a single indicator of vulnerability (Time to Hydraulic Failure -THF) and to assess mortality risk in future climate. We found that among circum-Mediterranean firs, a highTHFwas mainly due to high water losses after stomatal closure. Some operational indicators are good proxies ofTHF, however they are not available for all species, reduce a species to a single value and have the same limitations as species distribution models. We argue that the ecophysiological approach could help foresters in species selection and in estimating the risk faced by forest tree species in a changing climate. While accounting for the variability of traits, hydraulic models can be forced with different climatic scenarios allowing hydraulic failure risk assessment by the end of the century.HighlightsIntegrating ecophysiological traits in a process-based model allows the computation of an accurate indicator of vulnerability: the Time to Hydraulic Failure (THF).ForAbiesspecies, ”operational” indicators,i.e.indicators used by foresters and land managers, are mutually consistent and can be simplified proxies of the vulnerability to hydraulic failure.More research on intra-specific variability is needed to assess the evolutionary potential of Euro-MediterraneanAbiesspecies.
Glycolytic flux sustains human Th1 identity and effector function via STAT1 glycosylation
Authors: Abir Ariful Haque , Benz Julia , Frey Benjamin , Bruns Heiko , Gaipl Udo S. , Schober Kilian , Mougiakakos Dimitrios , Mielenz Dirk 
Published: February 2025

AbstractFate decisions of T helper (Th) cells are tightly linked to their metabolic states, but precise mechanistic links remain unknown, especially in humans. Usingin vitrostimulation in combination with gene editing we studied how metabolic regulation shapes human Th1 cell identity and effector function. Differentiated Th1 cells displayed elevated STAT1 phosphorylation at Tyr701 and Ser727 as well as heightened T-bet and IFNγ expression, which were dampened by CRISPR/Cas9-mediated STAT1 deletion. Metabolic profiling revealed enhanced glycolytic activity in Th1 in comparison to Act.T cells, evidenced by increased extracellular acidification rate, ATP production via glycolysis, glucose uptake, lactate secretion and NADH abundance. SCENITH analysis demonstrated elevated glycolysis-dependent anabolic activity of Th1 cells. Inhibition of glycolysis reduced IFNγ production and STAT1 phosphorylation independent of JAK1/2 activity, STAT1 abundance or SHP-2 activity, implicating glycolysis directly in sustaining STAT1-mediated Th1 functionality. Mechanistically, O-Glycosylation, facilitated by O-Glycosyltransferase, emerged as pivotal in modulating STAT1 activity, as evident through immunoprecipitation and Western blot analysis. Pharmaceutical O-Glycosyltransferase inhibition prevented Th1 differentiation as well as STAT1 O-glycosylation. CRISPR/Cas9 mediated mutation of the O-glycosylation sites Ser499 and Thr510 sites diminished STAT1 Ser727 phosphorylation and IFNγ synthesis. Together, this study highlights glycolysis as key regulator of human Th1 cell identity and effector function, with STAT1 O-Glycosylation selectively maintaining Th1 effector capacity. This mechanism could be explored to safeguard Th1 cells in antiviral immunity and autoimmunity.Graphical abstract
Recent thymic emigrants are preferentially recruited into the memory pool during persistent infection
Authors: Hilt Zachary T. , Reynaldi Arnold , Steinhilber Megan , Zhang Shide , Wesnak Samantha P. , Smith Norah L. , Davenport Miles P , Rudd Brian D. 
Published: February 2025

AbstractCytomegalovirus (CMV) leads to a unique phenomenon known as ‘memory inflation,’ where antigen-specific memory CD8+ T cells continue to accumulate in the peripheral tissues during the latent stage of infection. However, it is still not clear how the inflating pool of memory CD8+ T cells is generated and maintained. In this study, we used murine cytomegalovirus (MCMV) as a model of persistent infection and fate-mapping mice to determine the dynamics of CD8+ T cell recruitment into the memory pool. We found that neonatal exposure to CMV leads to an expansion of newly made CD8+ T cells (recent thymic emigrants, RTEs), which are maintained in the long-lived memory compartment. In contrast, CD8+ T cells made during the latent phase of infection (mature CD8+ T cells) contribute little to the memory pool. We also observed notable phenotypic differences between RTEs and mature cells. Whereas RTEs present at the time of infection gave rise to more effector memory cells, the cells produced later in infection were biased towards becoming central memory cells. Importantly, the preferential recruitment of RTEs into the effector memory pool also occurs during adult exposure to CMV. Collectively, these data demonstrate that persistent infection expands the RTE population, and timing of infection dictates whether neonatal or adult RTEs are ‘locked in’ to the memory pool.Author SummaryFollowing infection with CMV, CD8+ T cells accumulate in the blood and peripheral organs over time, a feature termed ‘memory inflation’. However, it is not clear whether memory inflation is due to the continuous recruitment of cells made during the latent stage of infection or expansion of CD8+ T cells that were present at the time of infection. To address this question, we used a fate-mapping mouse model and examined the recruitment of CD8+ T cells that were produced during different stages of infection. Surprisingly, we discovered that CD8+ T cells exported from the thymus just prior to infection are preferentially recruited and maintained in the memory pool. In contrast, CD8+ T cells made during the latent stage contribute minimally to the inflating pool and exhibit a less differentiated phenotype. These results provide a new conceptual framework for understanding how the memory pool is generated and maintained after persistent viral infection.
Proteomic Landscape of Pattern Triggered Immunity in the Arabidopsis Leaf Apoplast
Authors: Chen Hsiao-Chun , Newton Carter J. , Diaz Gustavo , Zheng Yaochao , Kong Feng , Yao Yao , Yang Li , Kvitko Brian H. 
Published: February 2025

AbstractThe apoplast is a critical interface in plant-pathogen interactions particularly in the context of pattern-triggered immunity (PTI), which is initiated by recognition of microbe-associated molecular patterns (PAMPs). Our study characterizes the proteomic profile of the Arabidopsis apoplast during PTI induced by flg22, a 22 amino acid bacterial flagellin epitope, to elucidate the output of PTI. Apoplastic washing fluid (AWF) was extracted with minimal cytoplasmic contamination for LC-MS/MS analysis. We observed consistent identification of PTI enriched and depleted peptides across replicates with limited correlation between total protein abundance and transcript abundance. We observed topological bias in peptide recovery of receptor-like kinases with peptides predominantly recovered from their ectodomains. Notably, tetraspanin 8, an exosome marker, was enriched in PTI samples. We additionally confirmed increased concentrations of exosomes during PTI. This study enhances our understanding of the proteomic changes in the apoplast during plant immune responses and lays the groundwork for future investigations into the molecular mechanisms of plant defense under recognition of pathogen molecular patterns.
IQI: Individualized Quantitative trait loci (QTL) Index
Authors: Xu Jing , Sun Chen , Tao Junxian , Wei Siyu , Dong Yu , Guo Xuying , Tian Hongsheng , Chen Haiyan , Ma Yingnan , Lyu Wenhua , Bi Shuo , Zou Yuping , She Wei , Zhang Chen , Kang Jingxuan , Kong Fanwu , Zhang Mingming , Lyu Hongchao , Jiang Yongshuai 
Published: February 2025

AbstractThere is currently no standard efficient method to precisely detecting and characterizing the difference of quantitative trait loci (QTL) effect among groups and/or populations at individual level. The main reason is that the traditional global regression based QTL analyses can only derive an overall effect of genetic variants on the quantitative trait in a specific group. It is a big challenge to obtain the individual-level QTL effect, which is necessary for accurate testing and precise characterization. Here, we first proposed an individualized QTL index (IQI) that represents the degree of regulation between genetic variants and the trait for each individual. Based on IQI, Differential QTL (DQ) analysis framework was presented to accurately detect the case-control differences in more detail. In addition, IQI can be a new feature/biomarker applied to various accurate bioinformatics analysis, such as differential analysis, disease prediction, and survival analysis. Overall, IQI affords a new perspective on pathogenic mechanisms and will facilitate the study of genetic regulation more detailedly and precisely.