bluuebunny/crossref_metadata_2025_split huggingface dataset:
Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations
Authors: Quintero Elena , Arroyo-Correa Blanca , Isla Jorge , Rodríguez-Sánchez Francisco , Jordano Pedro 
Published: February 2024

AbstractSpecies-level networks emerge as the combination of interactions spanning multiple individuals, and their study has received considerable attention over the past 30 years. However, less is known about the structure of interaction configurations within species, even though individuals are the actual interacting units in nature.We compiled 46 empirical, individual-based, interaction networks on plant-animal seed dispersal mutualisms, comprising 1037 plant individuals across 29 species from various regions. We compared the structure of individual-based networks to that of species-based networks and, by extending the niche concept to interaction assemblages, we explored individual plant specialization. Using a Bayesian framework to account for uncertainty derived from sampling, we examined how plant individuals “explore” the interaction niche of their populations.Both individual-based and species-based networks exhibited high variability in network properties, lacking remarkable structural and topological differences between them. Within populations, frugivores’ interaction allocation among plant individuals was highly heterogeneous, with one to three frugivore species dominating interactions. Regardless of species or bioregion, plant individuals displayed a variety of interaction profiles across populations, with a consistently small percentage of individuals playing a central role and exhibiting high diversity in their interaction assemblage. Plant populations showed variable mid to low levels of niche specialization; and individuals’ interaction niche “breadth” accounted for 70% of the population interaction diversity, on average.Our results highlight how downscaling from species to individual-based networks helps understanding the structuring of interactions within ecological communities and provide an empirical basis for the extension of niche theory to complex mutualistic networks.Significance StatementEcological interactions in nature occur between individual partners rather than species, and their outcomes determine fitness variation. By examining among-individual variation in interaction niches, we can bridge evolutionary and ecological perspectives to understand interaction biodiversity. This study investigates individual plant variation in frugivore assemblages worldwide, exploring how plant individuals “build” their interaction profiles with animal frugivores. The structure of networks composed of individuals was surprisingly similar to networks composed of species. Within populations, only a few plants played a key role in attracting a high diversity of frugivores, making them central to the overall network structure. Individuals actually interacted with a substantial diversity of partners, with individual niche “breadth” accounting for up to 70% of total interaction diversity, on average.
Photoprotective mechanisms inElysiaspecies hostingAcetabulariachloroplasts shed light on host-donor compatibility in photosynthetic sea slugs
Authors: Morelli Luca , Havurinne Vesa , Madeira Diana , Martins Patrícia , Cartaxana Paulo , Cruz Sónia 
Published: February 2024

AbstractSacoglossa sea slugs have garnered attention due to their ability to retain intracellular functional chloroplasts from algae, while degrading other algal cell components. While protective mechanisms that limit oxidative damage under excessive light are well documented in plants and algae, the photoprotective strategies employed by these photosynthetic sea slugs remain unresolved. Species within the genusElysiaare known to retain chloroplasts from various algal sources, but the extent to which the metabolic processes from the donor algae can be sustained by the sea slugs is unclear. By comparing their responses to high light conditions through kinetic analyses, molecular techniques, and biochemical assays, this study highlights significant differences between two photosyntheticElysiaspecies with chloroplasts derived from the green algaAcetabularia acetabulum. Notably,Elysia timidadisplayed remarkable tolerance to high light stress and sophisticated photoprotective mechanisms such as an active xanthophyll cycle, efficient D1 protein recycling, accumulation of heat-shock proteins and α-tocopherol. In contrast,Elysia crispataexhibited absence or limitations in these photoprotective strategies. Our findings emphasize the intricate relationship between the host animal and the stolen chloroplasts, highlighting different capacities to protect the photosynthetic organelle from oxidative damage.
AttentionPert: Accurately Modeling Multiplexed Genetic Perturbations with Multi-scale Effects
Authors: Bai Ding , Ellington Caleb , Mo Shentong , Song Le , Xing Eric 
Published: February 2024

AbstractGenetic perturbations (i.e. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited in the number of perturbation conditions they can measure. Computational methods can fill this gap by predicting perturbation effects under unseen conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge. We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the non-uniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene-gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-theart method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios.
Lineage-resolved analysis of embryonic gene expression evolution inC. elegansandC. briggsae
Authors: Large Christopher R. L. , Khanal Rupa , Hillier LaDeana , Huynh Chau , Kubo Connor , Kim Junhyong , Waterston Robert H. , Murray John I. 
Published: February 2024

AbstractWhat constraints govern the evolution of gene expression patterns across development remains a fundamental question of evolutionary biology. The advent of single-cell sequencing opens the possibility of learning these constraints by systematically profiling homologous cells across different organisms. The nematodeC. elegansis a well-studied model for embryonic development, and its invariant lineage that is conserved with otherCaenorhabditisspecies makes it an ideal model to directly compare gene expression between homologous progenitor and terminal cell types across evolution. We have measured the spatiotemporal divergence of gene expression across embryogenesis by collecting, annotating, and comparing the transcriptomes of homologous embryonic progenitors and terminal cell types, using a dataset comprising >200,000C. eleganscells and >190,000C. briggsaecells. We find a high level of similarity in gene expression programs between the species despite tens of millions of years of evolutionary divergence, consistent with their conserved developmental lineages. Even still, thousands of genes show divergence in their cell-type specific expression patterns, and these are enriched for categories involved in environmental response and behavior. Comparing the degree of expression conservation across cell types reveals that certain cell types such as neurons, have diverged more than others such as the intestine and body wall muscle. Taken together, this work identifies likely constraints on the evolution of developmental gene expression and provides a powerful resource for addressing diverse evolutionary questions.
XBB.1.5 monovalent booster improves antibody binding and neutralization against emerging SARS-CoV-2 Omicron variants
Authors: Jain Shilpi , Kumar Sanjeev , Lai Lilin , Linderman Susanne , Malik Ansa A. , Ellis Madison L. , Godbole Sucheta , Solis Daniel , Sahoo Malaya K. , Bechnak Kareem , Paredes Isabel , Tanios Ralph , Kazzi Bahaa , Dib Serena M. , Litvack Matthew B , Wimalasena Sonia T. , Ciric Caroline , Rostad Christina , West Richard , Teng I-Ting , Wang Danyi , Edupuganti Sri , Kwong Peter D. , Rouphael Nadine , Pinsky Benjamin A. , Douek Daniel C. , Wrammert Jens , Moreno Alberto , Suthar Mehul S. 
Published: February 2024

AbstractThe rapid emergence of divergent SARS-CoV-2 variants has led to an update of the COVID-19 booster vaccine to a monovalent version containing the XBB.1.5 spike. To determine the neutralization breadth following booster immunization, we collected blood samples from 24 individuals pre- and post-XBB.1.5 mRNA booster vaccination (∼1 month). The XBB.1.5 booster improved both neutralizing activity against the ancestral SARS-CoV-2 strain (WA1) and the circulating Omicron variants, including EG.5.1, HK.3, HV.1, XBB.1.5 and JN.1. Relative to the pre-boost titers, the XBB.1.5 monovalent booster induced greater total IgG and IgG subclass binding, particular IgG4, to the XBB.1.5 spike as compared to the WA1 spike. We evaluated antigen-specific memory B cells (MBCs) using either spike or receptor binding domain (RBD) probes and found that the monovalent booster largely increases non-RBD cross-reactive MBCs. These data suggest that the XBB.1.5 monovalent booster induces cross-reactive antibodies that neutralize XBB.1.5 and related Omicron variants.
Identification of differentially methylated single-nucleotide m6A sites by incorporating site-specific antibody specificity
Authors: Guo Yang , Wu Zehong , Cheng Weisheng , Ren Zhijun , Cun Yixian , Wang Jinkai 
Published: February 2024

ABSTRACTVarious genome-wide and transcriptome-wide technologies are based on antibodies, however, the specificity of antibodies on different targets has not been characterized or considered in the analyses. The antibody-based MeRIP-seq is the most widely used method to determine the locations of N6-methyladenosine (m6A) on RNAs, especially for differential m6A analyses. However, the antibody specificities in different RNA regions and their resulting technical biases in differential m6A analyses have not been evaluated. Here, we evaluated the m6A antibody specificities using 100 pairs of spike-in RNAs with known m6A levels at single sites. Based on two replicates with different m6A levels on spike-in RNAs, we realized the m6A antibody specificities of the m6A sites on spike-in RNAs were greatly varied and mainly determined by the surrounding sequences of the m6A sites. Moreover, the MeRIP-seq signal fold change is the function of the real difference in m6A levels as well as the m6A antibody specificity. We then trained a machine learning model to predict the m6A antibody specificities of given sequences and predicted the m6A specificities of all RNA sequences surrounding the known m6A motif DRACH throughout the human transcriptome. Finally, we developed a Hierarchical statistic model for Differential Analysis of m6A Sites (HDAMS) by taking advantage of the predicted m6A specificities. We found that HDAMS can accurately determine the differentially methylated single-nucleotide m6A sites and the output more functionally relevant results. Our study not only provides a powerful tool for differential m6A analyses but also provides a methodological framework for other antibody-based studies to incorporate antibody specificities.
The Vps13-like protein BLTP2 is pro-survival and regulates phosphatidylethanolamine levels in the plasma membrane to maintain its fluidity and function
Authors: Banerjee Subhrajit , Daetwyler Stephan , Bai Xiaofei , Michaud Morgane , Jouhet Juliette , Madhugiri Shruthi , Johnson Emma , Wang Chao-Wen , Fiolka Reto , Toulmay Alexandre , Prinz William A. 
Published: February 2024

AbstractLipid transport proteins (LTPs) facilitate nonvesicular lipid exchange between cellular compartments and have critical roles in lipid homeostasis1. A new family of bridge-like LTPs (BLTPs) is thought to form lipid-transporting conduits between organelles2. One, BLTP2, is conserved across species but its function is not known. Here, we show that BLTP2 and its homolog directly regulate plasma membrane (PM) fluidity by increasing the phosphatidylethanolamine (PE) level in the PM. BLTP2 localizes to endoplasmic reticulum (ER)-PM contact sites34, 5, suggesting it transports PE from the ER to the PM. We find BLTP2 works in parallel with another pathway that regulates intracellular PE distribution and PM fluidity6, 7. BLTP2 expression correlates with breast cancer aggressiveness8–10. We found BLTP2 facilitates growth of a human cancer cell line and sustains its aggressiveness in an in vivo model of metastasis, suggesting maintenance of PM fluidity by BLTP2 may be critical for tumorigenesis in humans.
The mechanism of mammalian proton-coupled peptide transporters
Authors: Lichtinger Simon M , Parker Joanne L , Newstead Simon , Biggin Philip C 
Published: February 2024

AbstractProton-coupled oligopeptide transporters (POTs) are of great pharmaceutical interest owing to their promiscuous substrate binding site that has been linked to improved oral bioavailability of several classes of drugs. Members of the POT family are conserved across all phylogenetic kingdoms and function by coupling peptide uptake to the proton electrochemical gradient. Cryo-EM structures and alphafold models have recently provided new insights into different conformational states of two mammalian POTs, SLC15A1 and SLC15A2. Nevertheless, these studies leave open important questions regarding the mechanism of proton and substrate coupling, while simultaneously providing a unique opportunity to investigate these processes using molecular dynamics (MD) simulations. Here, we employ extensive unbiased and enhanced-sampling MD to map out the full SLC15A2 conformational cycle and its thermodynamic driving forces. By computing conformational free energy landscapes in different protonation states and in the absence or presence of peptide substrate, we identify a likely sequence of intermediate protonation steps that drive inward-directed alternating access. These simulations identify key differences in the extracellular gate between mammalian and bacterial POTs, which we validate experimentally in cell-based transport assays. Our results from constant-PH MD and absolute binding free energy (ABFE) calculations also establish a mechanistic link between proton binding and peptide recognition, revealing key details underpining secondary active transport in POTs. This study provides a vital step forward in understanding proton-coupled peptide and drug transport in mammals and pave the way to integrate knowledge of solute carrier structural biology with enhanced drug design to target tissue and organ bioavailability.
Wavelet Phase Coherence of Ictal Scalp EEG-Extracted Muscle Activity (SMA) as a Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP)
Authors: Gravitis Adam C. , Sivendiran Krishram , Tufa Uilki , Zukotynski Katherine , Chinvarun Yotin , Devinsky Orrin , Wennberg Richard , Carlen Peter L. , Bardakjian Berj L. 
Published: February 2024

AbstractObjectiveApproximately 50 million people worldwide have epilepsy and 8-17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted.ApproachSeizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1-12 Hz, 13-30 Hz) to identify differences between the two groups.Main resultsIctal SMA in SUDEP patients showed a statistically higher average WPC value when compared to non-SUDEP patients for both frequency ranges. Area under curve for a cross-validated logistic classifier was 81%.SignificanceAverage WPC of ictal SMA is a candidate biomarker for early detection of SUDEP.
Generate Synthetic Data in R for a Hypothetical Alzheimer’s Disease Trial
Authors: Handels Ron , Jönsson Linus , Raket Lars Lau ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2024

AbstractINTRODUCTIONRepresentative data of recent Alzheimer’s Disease (AD) trials are difficult to obtain. We aimed to generate a synthetic version of an original real-world observational dataset, subsequently apply a plausible AD treatment effect, and make our method open-source available.METHODSSynthetic data was generated in the following steps: (1) Obtain real-world data from the ADNI study on demographic (age, sex, education), clinical (cognition: MMSE and ADAS; function: FAQ; composite cognition/function: CDR, ADCOMS) and biological (genetics: APOE4; cerebrospinal fluid: ABeta, Tau; imaging: PET-SUVR-centiloid) outcomes at baseline, 6, 12 and/or 18-month follow-up (35 variables), with missing data multiple-imputed to obtain 10 sets of 537 individuals. (2) Estimate (theoretical) minimum and maximum (all continuous variables) and proportions (all categorical variables). (3) Rescale to 0-1 range (continuous). (4) Estimate beta distribution shape parameters (method of moments; continuous). (5) Transform to cumulative probability distribution function (using shape parameters; continuous) and to cumulative probability (categorical). (6) Transform to a normal distribution. (7) Estimate variance-covariance matrix. (8) Generate random correlated normal data using Cholesky decomposition of variance-covariance. (9) Transform to cumulative probability distribution function. (10) Transform to beta distribution (using shape parameters; continuous). (11) Rescale to original range. (12) Keep half as control arm, and half as intervention arm, and estimate change from baseline. (13) Multiply intervention change from baseline with self-defined hypothetical relative treatment effect. We assumed correlations on normalized scale were similar to correlations on original scale. R code is available on github:https://github.com/ronhandels/synthetic-correlated-data.RESULTSThe synthetic distribution and mean over time showed large similarity to the original data (visually assessed). The absolute difference in pairwise correlations between original and synthetic data median was 0.02 (95th percentile=0.11, max=0.18).CONCLUSIONWe judged our method sufficiently valid to generate synthetic correlated plausible hypothetical trial results.
Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden
Authors: Beer Simone , Elmenhorst David , Bischof Gerard N. , Ramirez Alfredo , Bauer Andreas , Drzezga Alexander ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2024

AbstractAquaporin-4 (AQP4) is an integral component of the glymphatic system, today considered a crucial pathway for removing brain interstitial solutes like amyloid-β (Aβ). Evidence exists that genetic variation of AQP4 impacts Aβ clearance, clinical outcome in Alzheimer’s disease as well as sleep measures. We examined whether a risk score calculated from several AQP4 single-nucleotide polymorphisms (SNPs) is related to Aβ neuropathology in older cognitively unimpaired individuals. We used a machine learning approach with decision tree ensembles and explainable artificial intelligence (AI) to extract information on synergistic effects of AQP4 SNPs on brain amyloid burden from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. From this information, we formulated a sex-specific AQP4 SNP-based risk score and evaluated it on the basis of data from the screening process of the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) study. We found in both cohorts significant associations of the risk score with brain amyloid burden as well as amyloid positivity. The results support the hypothesis of an involvement of the glymphatic system, and particularly AQP4, in brain amyloid aggregation pathology. They suggest also that different AQP4 SNPs exert a synergistic effect on the build-up of brain amyloid burden.
Improving genetic risk modeling of dementia from real-world data in underrepresented populations
Authors: Fu Mingzhou , Valiente-Banuet Leopoldo , Wadhwa Satpal S. ,   UCLA Precision Health Data Discovery Repository Working Group,   UCLA Precision Health ATLAS Working Group, Pasaniuc Bogdan , Vossel Keith , Chang Timothy S. 
Published: February 2024

AbstractBACKGROUNDGenetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited.METHODSWe employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model withAPOEand polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation.RESULTSOur model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to theAPOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge.CONCLUSIONSOur study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.
Cryosectioning-enhanced super-resolution microscopy for single-protein imaging across cells and tissues
Authors: Stein Johannes , Ericsson Maria , Nofal Michel , Magni Lorenzo , Aufmkolk Sarah , McMillan Ryan B. , Breimann Laura , Herlihy Conor P. , Lee S. Dean , Willemin Andréa , Wohlmann Jens , Arguedas-Jimenez Laura , Yin Peng , Pombo Ana , Church George M. , Wu Chao-ting 
Published: February 2024

AbstractDNA-PAINT enables nanoscale imaging with virtually unlimited multiplexing and molecular counting. Here, we address challenges, such as variable imaging performance and target accessibility, that can limit its broader applicability. Specifically, we enhance its capacity for robust single-protein imaging and molecular counting by optimizing the integration of TIRF microscopy with physical sectioning, in particular, Tokuyasu cryosectioning. Our method, tomographic & kinetically enhanced DNA-PAINT (tkPAINT), achieves 3 nm localization precision across diverse samples, enhanced imager binding, and improved cellular integrity. tkPAINT can facilitate molecular counting with DNA-PAINT inside the nucleus, as demonstrated through its quantification of the in situ abundance of RNA Polymerase II in both HeLa cells as well as mouse tissues. Anticipating that tkPAINT could become a versatile tool for the exploration of biomolecular organization and interactions across cells and tissues, we also demonstrate its capacity to support multiplexing, multimodal targeting of proteins and nucleic acids, and 3D imaging.