bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the energetic and conformational properties of the sequence space connecting naturally occurring RNA tetraloop receptor motifs
Authors: Shin John H. , Cuevas Lena M. , Roy Rohit , Bonilla Steve L. , Al-Hashimi Hashim , Greenleaf William J , Herschlag Daniel 
Published: June 2024

AbstractFolded RNAs contain tertiary contact motifs whose structures and energetics are conserved across different RNAs. The transferable properties of RNA motifs simplify the RNA folding problem, but measuring energetic and conformational properties of many motifs remains a challenge. Here, we use a high-throughput thermodynamic approach to investigate how sequence changes alter the binding properties of naturally-occurring motifs, the GAAA tetraloop • tetraloop receptor (TLR) interactions. We measured the binding energies and conformational preferences of TLR sequences that span mutational pathways from the canonical 11ntR to two other natural TLRs, the IC3R and Vc2R. While the IC3R and Vc2R share highly similar energetic and conformational properties, the landscapes that map the sequence changes for their conversion from the 11ntR to changes in these properties differ dramatically. Differences in the energetic landscapes stem from the mutations needed to convert the 11ntR to the IC3R and Vc2R rather than a difference in the intrinsic energetic architectures of these TLRs. The conformational landscapes feature several non-native TLR variants with conformational preferences that differ from both the initial and final TLRs; these species represent potential branching points along the multidimensional sequence space to sequences with greater fitness in other RNA contexts with alternative conformational preferences. Our high-throughput, quantitative approach reveals the complex nature of sequence-fitness landscapes and leads to models for their molecular origins. Systematic and quantitative molecular approaches provide critical insights into understanding the evolution of natural RNAs as they traverse complex landscapes in response to selective pressures.
A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide
Authors: Dixit Pragyesh , Djafer-Cherif Ilyas , Shah Saumil , Traulsen Arne , Waclaw Bartlomiej 
Published: June 2024

AbstractMost cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.
Genome scale metabolic modelling of human gut microbes to inform rational community design
Authors: Ortiz Juan Pablo Molina , McClure Dale David , Holmes Andrew , Rice Scott Alan , Read Mark Norman , Shanahan Erin Rose 
Published: June 2024

AbstractBackgroundThe human gut microbiome plays a pivotal role in health and disease, influenced significantly by diet, particularly through the intake of digestion-resistant carbohydrates (DRCs). Emerging evidence underscores the potential of DRC supplementation in modulating the gut microbiome towards health-promoting metabolic outputs, notably through the fermentation of DRCs into short-chain fatty acids (SCFAs) including butyrate. However, the effectiveness of such interventions is hindered by the inherent complexity of microbial communities and the variable functional capacity of microbiomes across individuals. Improved understanding of gut ecology is necessary to move past interventions with transient benefits.ResultsThis study leverages genome-scale metabolic models (GEMs) to characterise the metabolic capabilities of 598 stable human gut colonising strains from the AGORA database. We infer the strains’ abilities to utilize dietary carbohydrates of varying complexities and produce metabolites that mediate interactions with other microbes and the host.Our analysis reveals a stratified functional landscape where prominent bacterial families show predispositions towards primary or secondary degrader roles based on their carbohydrate utilisation capabilities. Further, we identify metabolite production profiles that exceed phylogenetic variation in our sample. These results offer a comprehensive functional mapping of carbohydrate metabolism across a wide array of gut microbes, shedding light on the complex trophic networks underpinning the gut ecosystem.Supporting DRC-based interventions with rationally designed microbial communities can better guarantee the delivery of the intended health-promoting metabolic outputs. Applying our functional assessment, and principles of reverse ecology and network analysis, we propose a novel framework for the rational design gut microbial communities, where trophic networks are optimised to produce target metabolites from selected DRCs. Our results further suggest that this framework can predict resilient minimal communities, an important trait in the constantly changing human gut nutritional environment.ConclusionOur work provides novel insights into gut microbial ecology as well as intervention and consortia design. The identified metabolic capabilities of individual strains inform the rational design of a purpose-based microbial community to optimise butyrate production from inulin degradation. The framework we propose herein sets a foundation for future efforts aimed at the rational design of interventions that target the human gut microbiome to improve health.
Transferability of stream benthic macroinvertebrate distribution models to drought-related conditions
Authors: Medina Madariaga Graciela , Nguyen Hong Hanh , Kiesel Jens , Peters Kristin , Feld Christian K , Jaehnig Sonja C. , Cambas Yusdiel Torres 
Published: June 2024

AbstractFreshwater ecosystems increasingly face pressures from climate change induced extreme events, like droughts, posing significant threats to biodiversity. While Species Distribution Models (SDMs) serve as vital tools for predicting species responses to environmental shifts, their transferability to novel environmental conditions, especially during and after drought remains poorly understood. In this study, we delve into the transferability of SDMs for freshwater macroinvertebrates from drought-free to drought-influenced conditions. We examine how sensitive the transferability is to traits such as tolerance scores according to their distribution along longitudinal gradients, as well as the used modelling method. We constructed and validated SDMs for freshwater macroinvertebrates in a central German catchment under drought-free conditions using four different algorithms (Generalised linear models; GLM, Spatial Stream Networks; SSN, Random Forests; RF, and Maximum Entropy; MaxEnt). We then projected these models to environmental conditions influenced by drought, and obtained their transferability by computing the difference in accuracy when predicting under drought-free and drought-influenced conditions (AUCgap). Our findings reveal a marked reduction in SDM accuracy under drought conditions, illustrating the challenges of accurately predicting species distributions into novel environmental conditions. Our results show a slightly better transferability when using SSN and RF. In addition, we observed that SDM transferability can be influenced by species tolerance, with sensitive and tolerant species presenting higher AUCgap (i.e. lower transferability). Furthermore, we found that when sensitive species were modelled using SSNs, the AUCgap was reduced. Our study underscores the limitations of SDMs in capturing species responses to drought and advocates for integrating ecologically relevant predictors and modelling methods that account for the stream connectivity to allow for robust predictions. These considerations could enhance the ability of SDMs to effectively estimate the impacts of extreme events on freshwater biodiversity.
Lysophosphatidic acid receptor 1 influences disease severity in a mouse model of multiple sclerosis
Authors: Uemura Nagi , Matsuo Nagisa , Taniyama Kazunao , Nakajima Hiroki , Nagayasu Kazuki , Kaneko Shuji , Chun Jerold , Ueda Hiroshi , Shirakawa Hisashi 
Published: June 2024

AbstractMultiple sclerosis (MS), a chronic inflammatory disease affecting the central nervous system (CNS), is characterized by demyelination and axonal degeneration. Current treatments, which focus mainly on reducing lymphocyte infiltration into the CNS, are insufficient due to serious side effects and limited effectiveness; thus, identifying drugs with new mechanisms of action is crucial. Lysophosphatidic acid (LPA), a bioactive lipid produced by the enzyme autotaxin, may play a role in MS pathogenesis. Specifically, the LPA1subtype of LPA receptors is linked to release of inflammatory cytokines in the CNS, and to demyelination in the peripheral nervous system. Our study investigated the role of LPA1in a mouse model of MS. Knocking out the LPA1gene in mice with experimental autoimmune encephalomyelitis improved clinical outcomes and reduced demyelination. Additionally, the absence of LPA1reduced activation of Iba1-positive cells. Treatment with AM095, an LPA1antagonist, tended to improve clinical outcomes and reduce levels of inflammatory mediators. These findings indicate that activation of LPA1contributes to MS pathogenesis by promoting microglial activation and infiltration of peripheral immune cells.
The effect of host population genetic diversity on variation in metrics of parasite success
Authors: Paplauskas Sam , Duthie Brad , Tinsley Matthew 
Published: June 2024

ABSTRACTConventional wisdom suggests that populations with lower levels of genetic diversity are at a greater risk of the more harmful effects of disease. However, previous attempts to qualify this proposition have focused on measuring the mean, rather than the variability, in metrics of parasite success. Since the ability of host population genetic diversity to limit the spread of disease requires some specificity between hosts and parasites, and the benefits of host population genetic diversity in resistance to infection may depend on the respective parasite population genetic diversity, we propose a diversity-uncertainty model which predicts that the mean and variability in parasite success depend on a combination of host range and parasite population genetic diversity. By re-analyzing a dataset combining 48 studies collected by previous meta-analyses, we show that the effect of host population genetic diversity reduces the mean success of single-host, but not host generalist, parasites. We find evidence for our original hypothesis that the variability of parasite success depends on a combination of host population genetic diversity, parasite population genetic diversity and host range. Together, these results challenge conventional wisdom and have important implications for how genetic diversity can be better managed in host populations.
Nuclear phylogenomics of grasses (Poaceae) supports current classification and reveals repeated reticulation
Authors:   Grass Phylogeny Working Group III, Arthan Watchara , Baker William J. , Barrett Matthew D. , Barrett Russell L. , Bennetzen Jeffrey , Besnard Guillaume , Bianconi Matheus E. , Birch Joanne L. , Catalán Pilar , Chen Wenli , Christenhusz Maarten , Christin Pascal-Antoine , Clark Lynn G. , Columbus J. Travis , Couch Charlotte , Crayn Darren M. , Davidse Gerrit , Dransfield Soejatmi , Dunning Luke T. , Duvall Melvin R. , Ficinski Sarah Z. , Fisher Amanda E. , Fjellheim Siri , Forest Felix , Gillespie Lynn J. , Hackel Jan , Haevermans Thomas , Hodkinson Trevor R. , Huang Chien-Hsun , Huang Weichen , Humphreys Aelys M. , Jobson Richard W. , Kayombo Canisius J. , Kellogg Elizabeth A. , Kimeu John M. , Larridon Isabel , Letsara Rokiman , Li De-Zhu , Liu Jing-Xia , Londoño Ximena , Luke Quentin W.R. , Ma Hong , Macfarlane Terry D. , Maurin Olivier , McKain Michael R. , McLay Todd G.B. , Moreno-Aguilar Maria Fernanda , Murphy Daniel J. , Nanjarisoa Olinirina P. , Onjalalaina Guy E. , Peterson Paul M. , Rakotonasolo Rivontsoa A. , Razanatsoa Jacqueline , Saarela Jeffery M. , Simpson Lalita , Snow Neil W. , Soreng Robert J. , Sosef Marc , Thompson John J.E. , Traiperm Paweena , Verboom G. Anthony , Vorontsova Maria S. , Walsh Neville G. , Washburn Jacob D. , Watcharamongkol Teera , Waycott Michelle , Welker Cassiano A.D. , Xanthos Martin D. , Xia Nianhe , Zhang Lin , Zizka Alexander , Zuloaga Fernando O. , Zuntini Alexandre R. 
Published: June 2024

SummaryGrasses (Poaceae) comprise around 11,800 species and are central for human livelihoods and terrestrial ecosystems. Knowing their relationships and evolutionary history is key to comparative research and crop breeding. Advances in genome-scale sequencing allow for increased breadth and depth of phylogenomic analyses, making it possible to infer a new reference species tree of the family.We inferred a comprehensive species tree of grasses by combining new and published sequences for 331 nuclear genes from genome, transcriptome, target enrichment and shotgun data. Our 1,153-tip tree covers 79% of grass genera (including 21 genera sequenced for the first time) and all but two small tribes. We compared it to a 910-tip plastome tree.The nuclear phylogeny matches that of the plastome at most deep branches, with only a few instances of incongruence. Gene tree–species tree reconciliation suggests that reticulation events occurred repeatedly in the history of grasses.We provide a robust framework for the grass tree of life to support research on grass evolution, including modes of reticulation, and genetic diversity for sustainable agriculture.
Likelihood-based Finetuning of Protein Language Models for Few-shot Fitness Prediction and Design
Authors: Hawkins-Hooker Alex , Surana Shikha , Kmec Jakub , Bent Oliver , Duckworth Paul 
Published: June 2024

AbstractProtein language models (PLMs) implicitly learn distributional constraints on protein sequences upheld over the course of evolution. As a consequence, the sequence and mutation-level likeli-hoods of such models form effective zero-shot predictors of mutations. Although various schemes have been proposed for exploiting the distributional knowledge captured by PLMs to enhance supervised fitness prediction and sequence design tasks, a lack of head-to-head comparison across different prediction strategies and different classes of PLM has made it challenging to identify the best-performing methods. Our contribution is to extend previously proposed ranking-based loss functions to develop likelihood scoring functions forfamily-basedandmaskedPLMs. We demonstrate that in the low-data setting the best configurations outperform the current SOTA approach, which is based on frozen embeddings. Furthermore, we propose ensembling strategies that exploit the strong dependence of the mutational distributions learned by PLMs on sequence context, showing that they can be used to guide efficient optimisation strategies over fitness landscapes.
Energetics of the outer retina II: Calculation of a spatio-temporal energy budget in retinal pigment epithelium and photoreceptor cells based on quantification of cellular processes
Authors: Kiel Christina , Prins Stella , Foss Alexander JE , Luthert Philip J 
Published: June 2024

AbstractThe outer retina (OR) is highly energy demanding. Impaired energy metabolism combined with high demands are expected to cause energy insufficiencies that make the OR susceptible to complex blinding diseases such as age-related macular degeneration (AMD). Here, anatomical, physiological and quantitative molecular data were used to calculate the ATP expenditure of the main energy-consuming processes in three cell types of the OR for the night and two different periods during the day. The predicted energy demands in a rod dominated (perifovea) area are 1.33 × 1013ATP/s/mm2tissue in the night and 6.53 × 1012ATP/s/mm2tissue during the day with indoor light conditions. For a cone-dominated foveal area the predicted energy demands are 6.44 × 1012ATP/s/mm2tissue in the night and 6.75 × 1012ATP/s/mm2tissue with indoor light conditions during daytime. We propose the likely need of for diurnal/circadian shifts in energy demands to efficiently stagger all energy consuming processes. Our data provide insights into vulnerabilities in the aging OR and suggest that diurnal constraints may be important when considering therapeutic interventions to optimize metabolism.
“Energetics of the outer retina I: Estimates of nutrient exchange and ATP generation.”
Authors: Prins Stella , Kiel Christina , Foss Alexander JE , Zouache Moussa A , Luthert Philip J 
Published: June 2024

AbstractPhotoreceptors (PRs) are metabolically demanding and packed at high density, which presents a challenge for nutrient exchange between the associated vascular beds and the tissue. Motivated by the ambition to understand the constraints under which PRs function, in this study we have drawn together diverse physiological and anatomical data in order to generate estimates of the rates of ATP production per mm2of retinal surface area. With the predictions of metabolic demand in the companion paper, we seek to develop an integrated energy budget for the outer retina.It is known that rod PR number and the extent of the choriocapillaris (CC) vascular network that supports PRs both decline with age. To set the outer retina energy budget in the context of aging we demonstrate how, at different eccentricities, decline CC density is more than matched by rod loss in a way that tends to preserve nutrient exchange per rod. Together these finds provide an integrated framework for the study of outer retinal metabolism and how it might change with age.
Trajectory-centric Framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene module in osteogenesis
Authors: Han Litian , Ji Yaoting , Yu Yiqian , Ni Yueqi , Zeng Hao , Zhang Xiaoxin , Liu Huan , Zhang Yufeng 
Published: June 2024

AbstractOsteoblast differentiation is crucial for bone formation and maintaining skeletal integrity. Although it is now understood that this process exhibits significant heterogeneity across developmental stages and tissue microenvironments, the underlying mechanisms remain largely unexplored. In the present study, we introduceTrajAtlas, a comprehensive framework that addresses this gap in knowledge.TrajAtlascomprises four modules: a reference atlas (Differentiation Atlas), a differentiation model (Differentiation Model), a tool for differential pseudotime analysis (TrajDiff), and a method for pseudotemporal gene module detection (TRAVMap). By leveraging single-cell technologies,TrajAtlasoffers a systematic approach to exploring the multi-scale heterogeneity among cells, genes, and gene modules within population-level trajectories across diverse tissues and age groups. We systematically investigate the impact of age and injury on osteogenesis, providing new insights into osteoporosis and bone regeneration. In conclusion, our comprehensive framework offers novel insights into osteogenesis and provides a valuable resource for understanding the complexities of bone formation.Author SummaryOsteoblasts, the cells responsible for bone formation, can originate from various cellular sources. However, it’s unclear how different progenitor cells differentiate into osteoblasts, and how this process is influenced by factors such as age and tissue location. This knowledge gap stems from the lack of comprehensive databases and tools to decipher the differentiation process. In this study, we introduce TrajAtlas, a comprehensive framework designed to bridge this gap. To explore the cellular origins of osteoblasts, we constructed an atlas centered on osteogenesis. To answer how progenitor cells differentiate to osteoblasts, we developed a model that reveals the dynamic regulatory landscape during this process. To elucidate the influence of age and tissue location on differentiation, we built a tool for differential analysis. Furthermore, to identify conserved patterns of differentiation, we developed an approach to detect pseudotemporal gene modules. We validated the effectiveness of this framework by applying it to more datasets, unveiling novel cell states associated with injury. Notably, this framework focuses on dynamic processes, with the potential for broader applications in studying cell differentiation and complementing cell-centric analyses.
RNADiffFold: Generative RNA Secondary Structure Prediction using Discrete Diffusion Models
Authors: Wang Zhen , Feng Yizhen , Tian Qingwen , Liu Ziqi , Yan Pengju , Li Xiaolin 
Published: June 2024

AbstractRNA molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of RNA secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for RNA structure prediction, including energy-based and learning-based approaches, often depict RNA secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce RNADiffFold, an innovative generative prediction approach of RNA secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to refine the contact maps starting from a noise-infused state progressively. We also devise a potent conditioning mechanism that harnesses features extracted from RNA sequences to steer the model toward generating an accurate secondary structure. These features encompass one-hot encoded sequences, probabilistic maps generated from a pre-trained scoring network, and embeddings and attention maps derived from RNA-FM. Experimental results on both within- and cross-family datasets demonstrate RNADiffFold’s competitive performance compared with current state-of-the-art methods. Additionally, RNADiffFold has shown a notable proficiency in capturing the dynamic aspects of RNA structures, a claim corroborated by its performance on datasets comprising multiple conformations.