bluuebunny/crossref_metadata_2025_split huggingface dataset:
AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition
Authors: Outram Megan A. , Chen Jian , Broderick Sean , Li Zhao , Aditya Shouvik , Tasneem Nuren , Arndell Taj , Blundell Cheryl , Ericsson Daniel J. , Figueroa Melania , Sperschneider Jana , Dodds Peter N. , Williams Simon J. 
Published: November 2023

AbstractStem rust, caused by the fungal pathogenPuccinia graminis f. sp.tritici(Pgt) is a major threat for wheat production and global food security. Central to the success ofPgtis the secretion of proteinaceous effectors that promote infection and colonisation, while immunity in wheat is driven by receptor-mediated recognition of these effectors resulting in pathogen avirulence. Here, we report the crystal structure of the cysteine-rich effector AvrSr27, the third experimentally derived structure of aPgteffector. The AvrSr27 structure reveals a novel β-strand rich modular fold consisting of two structurally similar domains and confirms the poor prediction we obtained from the AlphaFold2-derived model. The highly prevalent cysteine residues within the protein facilitate the co-ordination of 4 zinc molecules. Utilising the structure, we show that the N-terminal domain of AvrSr27 is sufficient for immune recognition and interaction by Sr27. The 7-cys motif sequence in each AvrSr27 domain, which facilitates zinc binding, was also found in two haustorially-expressed, structurally homologousPgtproteins. Remarkably, despite relatively low sequence identity, we show that these proteins can associate with Sr27 and trigger cell death in heterologous systems and wheat protoplasts, albeit weaker than AvrSr27. Collectively, our findings have important implications for the field embarking on bespoke engineering of immunity receptors as solutions to plant disease.
Exploring functional conservationin silico: a new machine learning approach to RNA-editing
Authors: Zawisza-Álvarez Michał , Peñuela-Melero Jesús , Vegas Esteban , Reverter Ferran , Garcia-Fernàndez Jordi , Herrera-Úbeda Carlos 
Published: November 2023

AbstractAround 50 years from now, molecular biology opened the path to understand changes in forms, adaptations, complexity, or the basis of human diseases, through myriads of reports on gene birth, gene duplication, gene expression regulation, and splicing regulation, among other relevant mechanisms behind gene function. Here, with the advent of big data and artificial intelligence (AI), we focus on an elusive and intriguing mechanism of gene function regulation, RNA editing, in which a single nucleotide from an RNA molecule is changed with a remarkable impact in the increase of the complexity of transcriptome and proteome. We present a new generation approach to assess the functional conservation of the RNA-editing targeting mechanism using two AI learning algorithms, random forest (RF) and bidirectional long short-term memory (biLSTM) neural networks with attention layer. These algorithms combined with RNA-editing data coming from databases and variant calling from same-individual RNA and DNA-seq experiments from different species, allowed us to predict RNA-editing events using both primary sequence and secondary structure. Then, we devised a method for assessing conservation or divergence in the molecular mechanisms of editing completelyin silico: the cross-training analysis. This novel method not only helps to understand the conservation of the editing mechanism through evolution but could set the basis for understanding how it is involved in several human diseases.
The quantitative genetics of gene expression in Mimulus guttatus
Authors: Veltsos Paris , Kelly John K. 
Published: November 2023

AbstractGene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population ofMimulus guttatusand scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05) and that cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTL make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of “gene expression modules”, which has important implications for the evolution of gene expression and also how it is studied by geneticists.Author summaryMimulus guttatus (yellow monkeyflower) is a model for the study of quantitative trait evolution in natural populations. Research has focused mainly on whole organism traits like flower size or herbivore resistance, but the level of expression of a gene is also a quantitative trait. In this study, we dissect leaf transcriptome variation using a breeding design that estimates the contribution of individual loci to expression variation (eQTLs). We find at least rough agreement to the “oligogenic model” where a major locus (the cis regulatory region) generates much of the genetic variation in the population. While associations studies routinely characterize genetic effects as binary (the two alternatives at a single nucleotide polymorphism or “SNP”), this description proves insufficient for Mimulus. Most loci exhibit multiple, and in some cases, a continuum of alleles. We find that trans eQTLs have different features than cis eQTLs, both in terms of the diversity and genetic dominance of alleles. These genetic features of eQTLs are critical determinants of the “G matrix”, the genetic variances and covariances among all genes. The G matrix determines how gene expression will evolve under selection in response to changing environmental conditions. Our finding of large effect sizes and high allelic diversity suggests that the G matrix may be surprisingly malleable, even on ecological timescales.
Flower visitor groups show differential responses to individual and plot-level chemodiversity with consequences for plant fitness
Authors: Sasidharan Rohit , Grond Sean G. , Champion Stephanie , Eilers Elisabeth J. , Müller Caroline 
Published: November 2023

ABSTRACTChemodiversity, the diversity of specialised metabolites, plays a crucial role in mediating interactions between plants and animals, including insect herbivores and mutualists. Chemodiversity can be observed at both the individual and the population level. However, the impacts of chemodiversity at these two levels on interactions between plants and floral visitors, mainly pollinators and floral herbivores (florivores), are only poorly understood.This study aimed to investigate the effects of chemodiversity at both individual and plot level on pollinators and florivores, examining their mutual interactions. To investigate these questions, we used individuals of the perennialTanacetum vulgarediffering in their terpenoid composition, representing so-called chemotypes. We planted individuals of five distinct chemotypes in a common garden design with homogeneous (five plants of the same chemotype) and heterogeneous (five different chemotypes) plots and observed flower visits in dependence of individual chemotype and plot type. Seeds were collected from a subset of plants and germination rates determined.Our findings revealed that chemodiversity at the plot level significantly influenced pollinator visitation, with more visits on plants of heterogeneous plots. We also observed marginally more pollinators on one of the chemotypes grown in homogeneous plots. In contrast, chemotype but not plot type had a significant effect on florivore visits. Pollinator and florivore species richness did not vary with plot type. Furthermore, a negative correlation was observed between pollinator and florivore visits in one year, suggesting competitive interactions between these two groups. Germination rates were positively correlated with pollinator visits and affected by chemotype but not by florivore visits or plot type.Synthesis.Our study emphasizes the significance of the scale at which different chemical profiles are perceived by flower visitors, potentially influencing the reproductive fitness of plants. Exploring the ecology of these visitors and the varying selection pressures they exert on floral chemistry can help elucidating the evolutionary processes that maintain chemodiversity in natural environments.
Lowering mutant huntingtin by small molecules relieves Huntington’s disease symptoms and progression
Authors: Bahat Anat , Itzhaki Elad , Weiss Benjamin , Tolmasov Michael , Tsoory Michael , Kuperman Yael , Brandis Alexander , Shurrush Khriesto A. , Dikstein Rivka 
Published: November 2023

AbstractHuntington’s disease (HD) is an incurable inherited disorder caused by repeat expansion in the huntingtin gene (Htt). The mutant protein causes neuronal degeneration leading to severe motor and psychological abnormalities. Selective downregulation of the mutantHttexpression is considered the leading therapeutic approach for HD. We report the identification of novel small molecule inhibitors of Spt5-Pol II, SPI-24 and SPI-77, which selectively lower mutantHttmRNA and protein levels in HD cells. In the BACHD mouse model, their direct delivery to the striatum diminished mutantHttlevels, ameliorated mitochondrial dysfunction, restored BDNF expression and improved motor and anxious-like phenotypes. Pharmacokinetic studies revealed that these SPIs pass the blood-brain-barrier and prolonged subcutaneous injection or oral administration to early-stage mice significantly delayed disease deterioration. SPI-24 long-term treatment had no side effects or global changes in gene expression. Thus, lowering mutantHttlevels by small molecules can be an effective therapeutic strategy for HD.
Effects of menopause and high fat diet on metabolic outcomes in a mouse model of Alzheimer’s disease
Authors: Abi-Ghanem Charly , Salinero Abigail E. , Riccio David , Kelly Richard D. , Rybka Krystyna A. , Gannon Olivia J. , Kordit David , Kyaw Nyi-Rein , Mansour Febronia , Belanger Kasey M. , Thrasher Christina A. , Wang Matthew , Groom Emily , Smith Rachel M. , Brooks Heddwen L. , Robison Lisa S. , Zuloaga Damian G. , Zuloaga Kristen L. 
Published: November 2023

AbstractAbout two-thirds of those with Alzheimer’s disease (AD) are women, most of whom are post-menopausal. Menopause accelerates the risk for dementia by increasing the risk for metabolic, cardiovascular, and cerebrovascular diseases. Mid-life metabolic disease (e.g. obesity, diabetes, or prediabetes) is a well-known risk factor for dementia. A high fat diet can lead to poor metabolic health in both humans and rodents. The goal of this study was to determine the effects of menopause and high fat diet on metabolic outcomes in the AppNL-Fknock-in mouse model of Alzheimer’s disease. To model menopause, we used an accelerated ovarian failure model (4-vinylcyclohexene diepoxide, VCD). This ovary-intact model is more clinically relevant than an ovariectomy model, as mice go through a perimenopausal period. At 3 months of age, AppNL-Fmice were administered VCD or vehicle (oil) and then placed on either a control diet (10% fat) or a high fat diet (HF; 60% fat) and maintained on the diets until 10 months of age. Menopause led to metabolic impairment (weight gain and glucose intolerance) and further exacerbated obesity in response to a high fat diet. Menopause had independent effects on some serum metabolic health biomarkers (insulin) and synergic effects with HF diet on other markers (glucagon). Some metabolic effects of menopause may be centrally mediated, as menopause altered the expression of hypothalamic genes related to energy balance and increased microgliosis in the lateral hypothalamic nucleus. This work highlights the need to model endocrine aging in animal models of dementia and will contribute to further understanding the interaction between menopause and metabolic health in the context of AD.HighlightsIn a mouse model of AD, menopause, modeled by accelerated ovarian failure, leads to metabolic impairment.Menopause has independent effects on some serum metabolic health biomarkers (insulin) and synergic effects with HF diet on other markers (glucagon).Menopause alters the expression of hypothalamic energy balance related genes.Menopause leads to increased microgliosis in the lateral hypothalamic nucleus.
Moderate confirmation bias enhances collective decision-making in reinforcement-learning agents
Authors: Bergerot Clémence , Barfuss Wolfram , Romanczuk Pawel 
Published: November 2023

AbstractHumans tend to give more weight to information confirming their beliefs than to information that disconfirms them. Nevertheless, this apparent irrationality has been shown to improve individual decision-making under uncertainty. However, little is known about this bias’ impact on collective decision-making. Here, we investigate the conditions under which confirmation bias is beneficial or detrimental to collective decision-making. To do so, we develop a Collective Asymmetric Reinforcement Learning (CARL) model in which artificial agents observe others’ actions and rewards, and update this information asymmetrically. We use agent-based simulations to study how confirmation bias affects collective performance on a two-armed bandit task, and how resource scarcity, group size and bias strength modulate this effect. We find that a confirmation bias benefits group learning across a wide range of resource-scarcity conditions. Moreover, we discover that, past a critical bias strength, resource abundance favors the emergence of two different performance regimes, one of which is suboptimal. In addition, we find that this regime bifurcation comes with polarization in small groups of agents. Overall, our results suggest the existence of an optimal, moderate level of confirmation bias for collective decision-making.AUTHOR SUMMARYWhen we give more weight to information that confirms our existing beliefs, it typically has a negative impact on learning and decision-making. However, our study shows that moderate confirmation bias can actually improve collective decision-making when multiple reinforcement learning agents learn together in a social context. This finding has important implications for policymakers who engage in fighting against societal polarization and the spreading of misinformation. It can also inspire the development of artificial, distributed learning algorithms. Based on our research, we recommend not directly targeting confirmation bias but instead focusing on its underlying factors, such as group size, individual incentives, and the interactions between bias and the environment (such as filter bubbles).
Functional and evolutionary integration of a fungal gene with a bacterial operon
Authors: Sun Liang , David Kyle T. , Wolters John F. , Karlen Steven D. , Gonçalves Carla , Opulente Dana A. , LaBella Abigail Leavitt , Groenewald Marizeth , Zhou Xiaofan , Shen Xing-Xing , Rokas Antonis , Hittinger Chris Todd 
Published: November 2023

AbstractSiderophores are crucial for iron-scavenging in microorganisms. While many yeasts can uptake siderophores produced by other organisms, they are typically unable to synthesize siderophores themselves. In contrast,Wickerhamiella/Starmerella(W/S) clade yeasts gained the capacity to make the siderophore enterobactin following the remarkable horizontal acquisition of a bacterial operon enabling enterobactin synthesis. Yet, how these yeasts absorb the iron bound by enterobactin remains unresolved. Here, we demonstrate that Enb1 is the key enterobactin importer in the W/S-clade speciesStarmerella bombicola. Through phylogenomic analyses, we show thatENB1is present in all W/S clade yeast species that retained theenterobactin biosynthetic genes. Conversely, it is absent in species that lost theentgenes, except forStarmerella stellata, making this species the only cheater in the W/S clade that can utilize enterobactin without producing it. Through phylogenetic analyses, we infer thatENB1is a fungal gene that likely existed in the W/S clade prior to the acquisition of theentgenes and subsequently experienced multiple gene losses and duplications. Through phylogenetic topology tests, we show thatENB1likely underwent horizontal gene transfer from an ancient W/S clade yeast to the order Saccharomycetales, which includes the model yeastSaccharomyces cerevisiae, followed by extensive secondary losses. Taken together, these results suggest that the fungalENB1and bacterialentgenes were cooperatively integrated into a functional unit within the W/S clade that enabled adaptation to iron-limited environments. This integrated fungal-bacterial circuit and its dynamic evolution determines the extant distribution of yeast enterobactin producers and cheaters.
Spatial refuges and nutrient acquisition predict the outcome of evolutionary rescue in evolving microbial populations
Authors: Kelbrick Matthew , Fenton Andy , Parratt Steven , Hall James P. J. , O’Brien Siobhán 
Published: November 2023

AbstractMicrobial populations are often exposed to environmental stressors that impact their survival and evolution. Eco-evolutionary theory suggests microbial populations may be able to survive a stressor through “spatial refuges” – i.e., areas of low or reduced stress such as within biofilms. However, spatial refuges reduce a population’s access to nutrients, so may be detrimental depending on the severity of the stressor they are sheltering from. Using predictions from a general mathematical model, and the experimental evolution of the bacteriumPseudomonas fluorescensSBW25 under salinity stress and varying opportunities to form spatial refuges (i.e., agitated or non-agitated culture conditions), we show that spatial refuges can rescue a population from stressors only when nutrient levels are high. In the surviving high-salinity evolved populations (i.e., non-agitated culture conditions and high-nutrients), clones had an increased salinity resistance, indicating that spatial refuges can facilitate evolutionary rescue. Though whole genome resequencing did not reveal a single specific mutation associated with salt resistance, we found that clones evolved under control conditions (lower salt, high-nutrients, and no agitation) acquired mutations in a putative chemotaxis gene and showed increased motility. This indicates that spatial refuges under high salinity may also constrain adaptations to other environmental factors. Together, our combination of theory, laboratory experiment, and genome re-sequencing demonstrate the value and limits of spatial refuges in alleviating environmental stress within microbial populations.
AlphaFold-SFA: accelerated sampling ofcryptic pocketopening,protein-ligandbinding andallosteryby AlphaFold, slow feature analysis and metadynamics
Authors: Vats Shray , Bobrovs Raitis , Söderhjelm Pär , Bhakat Soumendranath 
Published: November 2023

AbstractSamplingrare eventsin proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by standard molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed asslow feature analysis(SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample ‘deep’ cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture accelerated ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.
Fast and Deep Phosphoproteome Analysis with the Orbitrap Astral Mass Spectrometer
Authors: Lancaster Noah M. , Sinitcyn Pavel , Forny Patrick , Peters-Clarke Trenton M. , Fecher Caroline , Smith Andrew J. , Shishkova Evgenia , Arrey Tabiwang N. , Pashkova Anna , Robinson Margaret Lea , Arp Nicholas , Fan Jing , Hansen Juli , Galmozzi Andrea , Serrano Lia R. , Rojas Julie , Gasch Audrey P , Westphall Michael S. , Stewart Hamish , Hock Christian , Damoc Eugen , Pagliarini David J. , Zabrouskov Vlad , Coon Joshua J. 
Published: November 2023

ABSTRACTOwing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology was benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We applied this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detected 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of novel phosphorylation events relevant to mitochondrial and brain biology.