bluuebunny/crossref_metadata_2025_split huggingface dataset:
Amphistomy increases leaf photosynthesis more in coastal than montane plants of Hawaiian ‘ilima (Sida fallax)
Authors: Triplett Genevieve , Buckley Thomas N. , Muir Christopher D. 
Published: November 2023

ABSTRACTPremise of the studyThe adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction.MethodsWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima (Sida fallax).Key resultsCoastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO2conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface.ConclusionsThis is the first direct experimental evidence that amphistomyper seincreases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO2to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
reconcILS: A gene tree-species tree reconciliation algorithm that allows for incomplete lineage sorting
Authors: Mishra Sarthak , Smith Megan L. , Hahn Matthew W. 
Published: November 2023

AbstractReconciliation algorithms provide an accounting of the evolutionary history of individual gene trees given a species tree. Many reconciliation algorithms consider only duplication and loss events (and sometimes horizontal transfer), ignoring effects of the coalescent process, including incomplete lineage sorting (ILS). Here, we present a new heuristic algorithm for carrying out reconciliation that accurately accounts for ILS by treating it as a series of nearest neighbor interchange (NNI) events. For discordant branches of the gene tree identified by last common ancestor (LCA) mapping, our algorithm recursively chooses the optimal history by comparing the cost of duplication and loss to the cost of NNI and loss. We demonstrate the accuracy of our new method, which we callreconcILS, using a new simulation engine (dupcoal) that can accurately generate gene trees produced by the interaction of duplication, loss, and ILS. Despite being a heuristic method, we show thatreconcILSis much more accurate than models that ignore ILS, and at least as accurate or better than leading methods that can model ILS, while also being able to handle much larger datasets. We demonstrate the use ofreconcILSby applying it to a dataset of 23 primate genomes, highlighting its accuracy compared to standard methods in the presence of large amounts of ILS.
Protein-Cadmium Interactions in Crowded Biomolecular Environments Probed by In-cell and Lysate NMR Spectroscopy
Authors: Katti Sachin S. , Igumenova Tatyana I. 
Published: November 2023

AbstractOne of the mechanisms by which toxic metal ions interfere with cellular functions is ionic mimicry, where they bind to protein sites in lieu of native metals Ca2+and Zn2+. The influence of crowded intracellular environments on these interactions is not well understood. Here, we demonstrate the application ofin-celland lysate NMR spectroscopy to obtain atomic-level information on how a potent environmental toxin cadmium interacts with its protein targets. The experiments, conducted in intactE. colicells and their lysates, revealed that Cd2+can profoundly affect the quinary interactions of its protein partners, and can replace Zn2+in both labile and non-labile protein structural sites without significant perturbation of the membrane binding function. Surprisingly, in crowded molecular environments Cd2+can effectively target not only all-sulfur and mixed sulfur/nitrogen but also all-oxygen coordination sites. The sulfur-rich coordination environments show significant promise for bioremedial applications, as demonstrated by the ability of the designed protein scaffold α3DIV to sequester intracellular cadmium. Our data suggests thatin-cellNMR spectroscopy is a powerful tool for probing interactions of toxic metal ions with their potential protein targets, and for the assessment of potency of sequestering agents.
SuPreMo: a computational tool for streamliningin silicoperturbation using sequence-based predictive models
Authors: Gjoni Ketrin , Pollard Katherine S. 
Published: November 2023

SummaryComputationally editing genome sequences is a common bioinformatics task, but current approaches have limitations, such as incompatibility with structural variants, challenges in identifying responsible sequence perturbations, and the need for vcf file inputs and phased data. To address these bottlenecks, we presentSequence Mutator forPredictiveModels (SuPreMo), a scalable and comprehensive tool for performingin silicomutagenesis. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize pathogenic variants or discover new functional sequences.Availability and ImplementationSuPreMo was written in Python, and can be run using only one line of code to generate both sequences and 3D genome disruption scores. The codebase, instructions for installation and use, and tutorials are on the Github page:https://github.com/ketringjoni/SuPreMo/tree/main.Contactkatherine.pollard@gladstone.ucsf.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Accurate identification ofHelicoverpa armigera–Helicoverpa zeahybrids using genome admixture analysis: implications for genomic surveillance
Authors: Trujillo D. , Mastrangelo T. , Estevez de Jensen C. , Verle Rodrigues J.C. , Lawrie R.D. , Massey S.E. 
Published: November 2023

AbstractHelicoverpa armigera, the cotton bollworm moth, is one of the world’s most important crop pests, and is spreading throughout the New World from its original range in the Old World. In Brazil, invasiveH.armigerahas been reported to hybridize with local populations ofHelicoverpa zea. The correct identification ofH.armigera-H.zeahybrids is important in understanding the origin, spread and future outlook for New World regions that are affected by outbreaks, given that hybridization can potentially facilitateH.zeapesticide resistance and host plant range via introgression ofH.armigeragenes. Here, we present a genome admixture analysis of high quality genome sequences generated from twoH.armigera-H.zeaF1 hybrids generated in two different labs. Our admixture pipeline predicts 48.8 %H.armigerafor both F1 hybrids, confirming its accuracy. Genome sequences from fiveH.zeaand oneH.armigerathat were generated as part of the study show no evidence of hybridization. Interestingly, we show that fourH.zeagenomes generated from a previous study are predicted to possess a proportion ofH.armigeragenetic material. Using unsupervised clustering to identify non-hybridizedH.armigeraandH.zeagenomes, 8511 ancestry informative markers (AIMs) were identified. Their relative frequencies are consistent with a minorH.armigeracomponent in the four genomes, however its origin remains to be established. We show that the size and quality of genomic reference datasets are critical for accurate hybridization prediction. Consequently, we discuss potential pitfalls in genome admixture analysis ofH.armigera-H.zeahybrids, and suggest measures that will improve such analyses.
Biobank-scale inference of multi-individual identity by descent and gene conversion
Authors: Browning Sharon R. , Browning Brian L. 
Published: November 2023

AbstractWe present a method for efficiently identifying clusters of identical-by-descent haplotypes in biobank-scale sequence data. Our multi-individual approach enables much more efficient collection and storage of identity by descent (IBD) information than approaches that detect and store pairwise IBD segments. Our method’s computation time, memory requirements, and output size scale linearly with the number of individuals in the dataset. We also present a method for using multi-individual IBD to detect alleles changed by gene conversion. Application of our methods to the autosomal sequence data for 125,361 White British individuals in the UK Biobank detects more than 9 million converted alleles. This is 2900 times more alleles changed by gene conversion than were detected in a previous analysis of familial data. We estimate that more than 250,000 sequenced probands and a much larger number of additional genomes from multi-generational family members would be required to find a similar number of alleles changed by gene conversion using a family-based approach.
Experimental evolution of multicellularity via cuboidal cell packing in fission yeast
Authors: Pineau Rozenn M. , Kahn Penelope C. , Lac Dung T. , Denning Mia , Wong Whitney , Ratcliff William C. , Bozdag G. Ozan 
Published: November 2023

AbstractThe evolution of multicellularity represents a major transition in life’s history, enabling the rise of complex organisms. Multicellular groups can evolve through multiple developmental modes, but a common step is the formation of permanent cell-cell attachments after division. The characteristics of the multicellular morphology which emerges has profound consequences for the subsequent evolution of a nascent multicellular lineage, but little prior work has examined these dynamics directly. Here we examine a widespread yet understudied emergent multicellular morphology: cuboidal packing. Extinct and extant multicellular organisms across the tree of life have evolved to form groups in which spherical cells divide but remain attached, forming approximately cubic subunits. To experimentally investigate the evolution of cuboidal cell packing, we used settling selection to favor the evolution of simple multicellularity in unicellular, sphericalSchizosaccharomyces pombeyeast. Multicellular clusters with cuboidal organization rapidly evolved, displacing the unicellular ancestor. These clusters displayed key hallmarks of an evolutionary transition in individuality: groups possess an emergent life cycle driven by physical fracture, group size is heritable, and they respond to group-level selection via multicellular adaptation. In 2/5 lineages, group formation was driven by mutations in theACE2gene, preventing daughter cell separation after division. Remarkably,ACE2mutations also underlie the transition to multicellularity inSaccharomyces cerevisiaeandC. galabrata, lineages last shared a common ancestor>300 million years ago. Our results provide insight into the evolution of cuboidal cell packing, an understudied multicellular morphology, and highlights the deeply convergent potential for a transition to multicellular individuality within fungi.
Higher Expression of Denervation-responsive Genes is Negatively Associated with Muscle Volume and Performance Traits in the Study of Muscle, Mobility and Aging (SOMMA)
Authors: Lukasiewicz Cole J. , Tranah Gregory J. , Evans Daniel S. , Coen Paul M. , Barnes Haley N. , Huo Zhiguang , Esser Karyn A. , Lane Nancy E. , Kritchevsky Steven B. , Newman Anne B. , Cummings Steven R. , Cawthon Peggy M. , Hepple Russell T. 
Published: November 2023

AbstractWith aging skeletal muscle fibers undergo repeating cycles of denervation and reinnervation. In approximately the 8thdecade of life reinnervation no longer keeps pace, resulting in the accumulation of persistently denervated muscle fibers that in turn cause an acceleration of muscle dysfunction. The significance of denervation in important clinical outcomes with aging is poorly studied. The Study of Muscle, Mobility and Aging (SOMMA) is a large cohort study with the primary objective to assess how aging muscle biology impacts clinically important traits. Using transcriptomics data from vastus lateralis muscle biopsies in 575 participants we have selected 49 denervation-responsive genes to provide insights to the burden of denervation in SOMMA, to test the hypothesis that greater expression of denervation-responsive genes negatively associates with SOMMA participant traits that included time to walk 400 meters, fitness (VO2peak), maximal mitochondrial respiration, muscle mass and volume, and leg muscle strength and power. Consistent with our hypothesis, increased transcript levels of: a calcium-dependent intercellular adhesion glycoprotein (CDH15), acetylcholine receptor subunits (Chrna1, Chrnd, Chrne), a glycoprotein promoting reinnervation (NCAM1), a transcription factor regulating aspects of muscle organization (RUNX1), and a sodium channel (SCN5A) were each negatively associated with at least 3 of these traits. VO2peakand maximal respiration had the strongest negative associations with 15 and 19 denervation-responsive genes, respectively. In conclusion, the abundance of denervation-responsive gene transcripts is a significant determinant of muscle and mobility outcomes in aging humans, supporting the imperative to identify new treatment strategies to restore innervation in advanced age.
CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
Authors: Jin Suoqin , Plikus Maksim V. , Nie Qing 
Published: November 2023

AbstractRecent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is the integration between known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat that infers and analyzes cell-cell communication networks from single-cell RNA-sequencing (scRNA-seq) data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass action-based model, which incorporates the core interaction between ligands and receptors with multi-subunit structure along with modulation by cofactors. CellChat v2 is an updated version that includes direct incorporation of spatial locations of cells, if available, to infer spatially proximal cell-cell communication, additional comparison functionalities, expanded database of ligand-receptor pairs along with rich annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 that can be used for both scRNA-seq and spatially resolved transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered signaling across different datasets. The key steps of applying CellChat v2 to spatially resolved transcriptomics are described in detail. The R implementation of CellChat v2 toolkit and tutorials with the graphic outputs are available athttps://github.com/jinworks/CellChat. This protocol typically takes around 20 minutes, and no specialized prior bioinformatics training is required to complete the task.
From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks
Authors: Zhu Hao , Slonim Donna K. 
Published: November 2023

AbstractUnderstanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inherent noise in the data. Here we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models focusing on the regulatory effects among feature variables. RegDiffusion introduces Gaussian noise to the input data following a diffusion schedule and uses a neural network with a parameterized adjacency matrix to predict the added noise. We show that using this process, GRNs can be learned effectively with a surprisingly simple model architecture. In our benchmark experiments, RegDiffusion shows superior performance compared to several baseline methods in multiple datasets. We also demonstrate that RegDiffusion can infer biologically meaningful regulatory networks from real-world single-cell data sets with over 15,000 genes in under 5 minutes. This work not only introduces a fresh perspective on GRN inference but also highlights the promising capacity of diffusion-based models in the area of single-cell analysis. The RegDiffusion software package and experiment data are available athttps://github.com/TuftsBCB/RegDiffusion.
The FXR1 network acts as signaling scaffold for actomyosin remodeling
Authors: Chen Xiuzhen , Fansler Mervin M. , Janjoš Urška , Ule Jernej , Mayr Christine 
Published: November 2023

SummaryIt is currently not known whether mRNAs fulfill structural roles in the cytoplasm. Here, we report the FXR1 network, an mRNA-protein (mRNP) network present throughout the cytoplasm, formed by FXR1-mediated packaging of exceptionally long mRNAs. These mRNAs serve as underlying condensate scaffold and concentrate FXR1 molecules. The FXR1 network contains multiple protein binding sites and functions as a signaling scaffold for interacting proteins. We show that it is necessary for RhoA signaling-induced actomyosin reorganization to provide spatial proximity between kinases and their substrates. Point mutations in FXR1, found in its homolog FMR1, where they cause Fragile X syndrome, disrupt the network. FXR1 network disruption prevents actomyosin remodeling—an essential and ubiquitous process for the regulation of cell shape, migration, and synaptic function. These findings uncover a structural role for cytoplasmic mRNA and show how the FXR1 RNA-binding protein as part of the FXR1 network acts as organizer of signaling reactions.
Female sex steroids and epilepsy: Part 2. A practical and human focus on catamenial epilepsy
Authors: Alshakhouri Malak , Sharpe Cynthia , Bergin Peter , Sumner Rachael L. 
Published: November 2023

AbstractCatamenial epilepsy is the best described and most researched sex steroid‐specific seizure exacerbation. Yet despite this there are no current evidence‐based treatments, nor an accepted diagnostic tool. The best tool we currently have is tracking seizures over menstrual cycles; however, the reality of tracking seizures and menstrual cycles is fraught with challenges. In Part 1 of this two‐part review, we outlined the often complex and reciprocal relationship between seizures and sex steroids. An adaptable means of tracking is required. In this review, we outline the extent and limitations of current knowledge on catamenial epilepsy. We use sample data to show how seizure exacerbations can be tracked in short/long and even irregular menstrual cycles. We describe how seizure severity, an often overlooked and underresearched form of catamenial seizure exacerbation, can also be tracked. Finally, given the lack of treatment options for females profoundly affected by catamenial epilepsy, Section 3 focuses on current methods and models for researching sex steroids and seizures as well as limitations and future directions. To permit more informative, mechanism‐focused research in humans, the need for both a consistent classification of catamenial epilepsy and an objective biomarker is highlighted.
To be traceable and responsive: blockchain adoption and information sharing in a fresh produce supply chain
Authors: Ma Songxuan , Dan Bin , Li Mengli , Zhou Maosen 
Published: November 2023

AbstractThis study targets a fresh produce supply chain that includes a retailer owning private demand information, a supplier who may adopt blockchain, and a 3PL (Third Party Logistics) engaging in freshness keeping. By developing a multi‐stage game model, we study the blockchain adoption strategy and the information‐sharing strategy considering information transmission between the 3PL and the supplier and analyze the interaction of these strategies. We find that under the case of moderate blockchain cost, the retailer could encourage the supplier to adopt blockchain by sharing information strategically. In that case, the retailer would actively share with the supplier and the 3PL for fresh produce with low freshness sensitivity and serious consumer distrust. Otherwise, due to the dominant impact of information transmission, the retailer has to keep private information if consumers are less concerned about freshness, while it has to share information if consumers are more concerned about freshness.
Species associations in joint species distribution models: from missing variables to conditional predictions
Authors: Vallé Clément , Poggiato Giovanni , Thuiller Wilfried , Jiguet Frédéric , Princé Karine , Le Viol Isabelle 
Published: November 2023

AbstractAimThe abundance and distribution of multiple species are interconnected through various mechanisms (e.g. biotic interactions or common responses to the environment) shaping communities. Joint species distribution models (jSDM) have been introduced as a potential tool to integrate these mechanisms when modelling multiple species distributions, by inferring a residual matrix of species associations that could inform on biotic interactions. However, the direct link between these residual associations and biotic interactions has been challenged. Here, we test how the data type, resolution and sampling size affect the species associations identified by jSDMs and their benefits for predicting species given the known state of others (i.e. conditional prediction).LocationFrance.TaxonBirds.MethodUtilizing standardized co‐abundances of 40 common bird species, across 7040 monitoring sites, and eight environmental variables at high resolution (200 m), we compared jSDM residual associations across data types (abundance vs. occurrence), resolution and sampling size. Additionally, we investigated correlations between residual associations and species functional similarities (eight traits). We then assessed to what extent residual associations contain valuable information for conditional predictions.ResultsOur results show that species associations identified by jSDM are greatly influenced by data resolution and sampling size rather than data types (abundance vs. occurrence). We find positive correlations between species associations and functional similarity that challenges the inference of negative biotic interactions expected from niche partitioning. However, retrieving these high‐resolution residual species associations for conditional predictions enhanced predictive quality for all species (+235% on average), potentially synthesizing missing variables difficult to capture in the field.Main ConclusionsWe highlight that species associations identified by jSDM using fine‐resolution co‐abundance datasets do not retrieve biotic interactions expected from niche partitioning (i.e. positive correlation with functional similarity) but probably missing environmental variables. Nonetheless, these residual associations contain valuable information to enhance predictive performance through currently underutilized conditional predictions.