bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hexokinase-I directly binds to a charged membrane-buried glutamate of mitochondrial VDAC1 and VDAC2
Authors: Bieker Sebastian , Timme Michael , Woge Nils , Hassan Dina G. , Brown Chelsea M. , Marrink Siewert J. , Melo Manuel N. , Holthuis Joost C. M. 
Published: July 2024

AbstractBinding of hexokinase HKI to mitochondrial voltage-dependent anion channels (VDACs) has far-reaching physiological implications. However, the structural basis of this interaction is unclear. Combining computer simulations with experiments in cells, we here show that complex assembly relies on intimate contacts between theN-terminal α-helix of HKI and a charged membrane-buried glutamate on the outer wall of VDAC1 and VDAC2. Protonation of this residue blocks complex formation in silico while acidification of the cytosol causes a reversable release of HKI from mitochondria. Membrane insertion of HKI occurs adjacent to the bilayer-facing glutamate where a pair of polar channel residues mediates a marked thinning of the cytoplasmic leaflet. Disrupting the membrane thinning capacity of VDAC1 dramatically impairs its ability to bind HKI in silico and in cells. Our data reveal key topological and mechanistic insights into HKI-VDAC complex assembly that may benefit the development of therapeutics to counter pathogenic imbalances in this process.
Impact of vertebrate host parasitaemia onPlasmodiumdevelopment within mosquitoes
Authors: Isaïa Julie , Baur Molly , Wassef Jérôme , Monod Sarah , Glaizot Olivier , Christe Philippe , Pigeault Romain 
Published: July 2024

AbstractBackgroundIn vector-borne diseases, invertebrate hosts are exposed to highly variable quantities of parasites during their blood meal. This heterogeneity may partly explain the overdispersed distribution of parasites within the vector population, as well as the variability in the extrinsic incubation period (EIP) of the parasite. Indeed, the quantity of parasites ingested is often considered as a good predictor of the quantity of parasites that will develop within the vectors, as well as the speed at which they will develop (EIP). However, density-dependent processes can strongly influence the relationship between parasite burden in the vertebrate host and in vectors, making this relationship not always clear.MethodsHere, we used the avian malaria system to investigate whether the proportion of red blood cells infected by sexual and/or asexual stages of malaria parasite influences the intensity of malaria infection and the EIP ofPlasmodiumwithin the invertebrate vectors. For this purpose, we have experimentally infected twelve vertebrate hosts in order to generate a range of intensity of infection. More than a thousand mosquitoes took a blood meal on these hosts and the development ofPlasmodiumwithin the vectors was followed for more than 20 days.ResultsThe main finding presented in this study reveals a negative relationship between the intensity of infection in the vertebrate host and the EIP. Four days were sufficient for 10% of infected mosquitoes fed on the most infected hosts to become infectious. However, the number of transmissible stages did not significantly vary according to the vertebrate host intensity of infection.ConclusionWhile the quantity of ingested parasites had no impact on the density of transmissible stages in infectious mosquitoes, the EIP was affected. Studies have demonstrated that small changes in the EIP can have a significant effect on the number of mosquitoes living long enough to transmit parasites. Here, we observed a difference of 4-6 days in the detection of the first transmissible stages, depending on the intensity of infection of the bitten vertebrate host. Considering that a gonotrophic cycle lasts 3-4 days, the shortened EIP observed here may have significant effects onPlasmodiumtransmission.
Prediction of plant resistance proteins using alignment-based and alignment-free approaches
Authors: Gahlot Pushpendra Singh , Choudhury Shubham , Bajiya Nisha , Kumar Nishant , Raghava Gajendra P. S. 
Published: July 2024

AbstractPlant Disease Resistance (PDR) proteins are critical in identifying and killing plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as BLAST for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning-based methods using compositional features of proteins. Our machine learning-based model, developed using compositional features of proteins, achieved a maximum performance AUROC of 0.92. The performance of our model improved significantly from AUROC of 0.92 to 0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best machine learning model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, “PlantDRPpred” (https://webs.iiitd.edu.in/raghava/plantdrppred).HighlightsDevelopment of a Machine-learning model for resistance protein prediction.Used alignment-based and alignment-free ensemble methods.Web server development and standalone package.Prediction and design of PDR proteins.
Dietary iron deficiency impairs effector function of memory T cells following influenza infection
Authors: Bradley Marissa C. , Gray Joshua , Carpia Francesca La , Idzikowski Emma , Guyer Rebecca , Pethe Kalpana , Hod Eldad A. , Connors Thomas J. 
Published: July 2024

ABSTRACTThe establishment of memory T cell responses is critical to protection against pathogens and is influenced by the conditions under which memory formation occurs. Iron is an essential micronutrient for multiple immunologic processes and nutritional deficiency is a common problem worldwide. Despite its prevalence, the impact of nutritional iron deficiency on the establishment of memory T cell responses is not fully understood. In this study we investigate the impact of nutritional iron deficiency on the generation, phenotype, and function of memory T cell responses using a murine model of dietary iron modulation in the context of influenza infection. Iron deficient mice have decreased systemic iron levels and develop significant anemia. Increased T cell expression of the transferrin receptor (CD71) is seen in iron deficient mice at baseline. During primary influenza infection, iron deficient mice experience increased weight loss and phenotypic evidence of impairments in T cell activation. Following recovery from infection, iron deficient mice generate increased influenza specific memory T cells which exhibit impaired ability to produce IFNγ, most notably within the lung. Importantly, the ability to produce IFNγ and TNFα is not recovered by co-culture with iron replete dendritic cells, suggesting a T cell intrinsic alteration in functional memory formation. Altogether, these results isolate a critical effect of nutritional iron deficiency on T cell memory development and function.
FuncFetch: An LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts
Authors: Smith Nathaniel , Yuan Xinyu , Melissinos Chesney , Moghe Gaurav 
Published: July 2024

AbstractMotivationThousands of genomes are publicly available, however, most genes in those genomes have poorly defined functions. This is partly due to a gap between previously published, experimentally-characterized protein activities and activities deposited in databases. This activity deposition is bottlenecked by the time-consuming biocuration process. The emergence of large language models (LLMs) presents an opportunity to speed up text-mining of protein activities for biocuration.ResultsWe developed FuncFetch — a workflow that integrates NCBI E-Utilities, OpenAI’s GPT-4 and Zotero — to screen thousands of manuscripts and extract enzyme activities. Extensive validation revealed high precision and recall of GPT-4 in determining whether the abstract of a given paper indicates presence of a characterized enzyme activity in that paper. Provided the manuscript, FuncFetch extracted data such as species information, enzyme names, sequence identifiers, substrates and products, which were subjected to extensive quality analyses. Comparison of this workflow against a manually curated dataset of BAHD acyltransferase activities demonstrated a precision/recall of 0.86/0.64 in extracting substrates. We further deployed FuncFetch on nine large plant enzyme families. Screening 27,120 papers, FuncFetch retrieved 32,605 entries from 5547 selected papers. We also identified multiple extraction errors including incorrect associations, non-target enzymes, and hallucinations, which highlight the need for further manual curation. The BAHD activities were verified, resulting in a comprehensive functional fingerprint of this family and revealing that ∼70% of the experimentally characterized enzymes are uncurated in the public domain. FuncFetch represents an advance in biocuration and lays the groundwork for predicting functions of uncharacterized enzymes.Availability and ImplementationCode and minimally-curated activities available at:https://github.com/moghelab/funcfetchandhttps://tools.moghelab.org/funczymedb
Interplay of Light, Melatonin, and Circadian Genes in Skin Pigmentation Regulation
Authors: Bertolesi Gabriel E. , Debnath Nilakshi , Heshami Neda , Bui Ryan , Zadeh-Haghighi Hadi , Simon Christoph , McFarlane Sarah 
Published: July 2024

AbstractHighlightsCircadian pigmentation of tadpoles in vivo is mainly driven by melatoninLight and melatonin differentially regulate proliferationMelatonin mimics the expression of circadian core genes in the dark phaseDeregulation of the circadian rhythm inhibits melanin synthesisCircadian regulation of skin pigmentation is essential for thermoregulation, UV protection, and synchronization of skin cell renewal. This regulation involves both cell-autonomous photic responses and non-cell-autonomous hormonal control, particularly through melatonin produced in a light-sensitive manner. Photosensitive opsins, cryptochromes, and melatonin regulate circadian rhythms in skin pigment cells. We studied light/dark cycles and melatonin coordination in melanin synthesis and cell proliferation ofXenopus laevismelanophores.In vivo, tadpole pigmentation shows robust circadian regulation mainly hormone-driven, in that isolated melanophores respond strongly to melatonin but only slightly to light. Melanophore proliferation is faster in the dark and slower with melatonin compared to a 12/12 light/dark cycle. Expression of circadian core genes (clock, bmal1, per1, per2, per3, cry1, cry2, and cry4) in melatonin-treated cells during the light phase mimics dark phase expression. Individual Cry overexpression did not affect melanisation or cell proliferation, likely due to functional redundancy. Melanin synthesis was inhibited by circadian cycle deregulation through: a) pharmacological inhibition of Cry1 and Cry2 degradation with KL001, b) continuous light or dark conditions, and c) melatonin treatment. Our findings suggest that circadian cycle regulation, rather than proliferative capacity, alters melanisation of melanophores.SignificanceCircadian rhythms are a highly conserved phenomenon in nature. In vertebrates, the modification of skin pigmentation and epidermal cell renewal in response to the environmental light-dark cycle are crucial physiological adaptations that serve various purposes, including thermoregulation, reducing ultraviolet damage, and regulating skin stem cell proliferation. Our observations indicate that,in vivo, the circadian regulation of skin pigmentation is more influenced by cycling-melatonin levels than light/dark. The deregulation of the circadian cell cycle through various mechanisms all inhibited melanisation while cell proliferation was increased or reduced, suggesting that proliferation and melanisation are mechanistically dissociated responses.Graphical Abstract
Genetic variants affect diurnal glucose levels throughout the day
Authors: Sinnott-Armstrong Nasa , Strausz Satu , Urpa Lea , Abner Erik , Valliere Jesse ,   Estonian Biobank Research Team, Palta Priit , Dashti Hassan S , Daly Mark , Pritchard Jonathan K , Saxena Richa , Jones Samuel E , Ollila Hanna M 
Published: July 2024

AbstractCircadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. However, the genetic variants that contribute to temporal control of glucose levels have not been previously examined. Using data from 420,000 individuals from the UK Biobank and replicating our findings in 100,000 individuals from the Estonian Biobank, we show that diurnal serum glucose is under genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B (MTNR1B)(rs10830963, P = 1e-22) and a canonical circadian pacemaker gene Cryptochrome 2 (CRY2)loci (rs12419690, P = 1e-16). Furthermore, we show that sleep modulates serum glucose levels and the genetic variants have a separate mechanism of diurnal control. Finally, we show that these variants independently modulate risk of type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight variation atMTNR1BandCRY2as temporal regulators for glucose levels.
Operational lessons learned from simulating an elimination response to a transboundary animal disease in wild animals
Authors: Chalkowski K , Pepin KM , Lavelle MJ , Miller RS , Fischer J , Brown VR , Glow M , Smith B , Cook S , Kohen K , Sherburne S , Smith H , Leland B , VerCauteren KC , Snow NP 
Published: July 2024

AbstractTransboundary animal disease (TAD) introductions can have myriad economic, ecological, and societal impacts. When TADs are introduced into wild species, rapid and intense control efforts to reduce wild animal host populations are sometimes needed to eliminate the disease and prevent endemicity and spillover to domestic animal populations. Yet, such intensive efforts are non-trivial, and the rarity of TAD introductions means that personnel rarely have direct experience with these types of operations. Thus, explicit assessments of operational challenges for these kinds of efforts can provide direction to build emergency response preparedness capacity. Here, we simulated a TAD control effort in response to initial detection of a hypothetical index case of a TAD in wild pigs (Sus scrofa) (e.g., African swine fever; ASF). We used three removal methods (aerial control, trapping, and an experimental toxic bait). Then, we conducted an after-action assessment to identify operational challenges for rapidly reducing a population of invasive wild pigs within a simulated outbreak zone. We also simulated carcass recoveries of dispatched pigs, similar to what might be conducted during a response to a TAD with carcass-based transmission (e.g., ASF virus). Here, we describe operational challenges identified during our effort, alongside technological development solutions anda prioristrategy needs to improve TAD response operation outcomes.
Toward improved uncertainty quantification in predictions of forest dynamics: A dynamical model of forest change
Authors: Itter Malcolm , Finley Andrew O. 
Published: July 2024

AbstractModels of forest dynamics are an important tool to understand and predict forest responses to global change. Despite recent model development, predictions of forest dynamics under global change remain highly variable reflecting uncertainty in future conditions, forest demographic processes, and the data used to parameterize and validate models. Quantifying this uncertainty and accounting for it when making adaptive management decisions is critical to our ability to conserve forest ecosystems in the face of rapidly changing conditions. Dynamical spatiotemporal models (DSTMs) are a particularly powerful tool in this setting given they quantify and partition uncertainty in demographic models and noisy forest observations, propagate uncertainty to predictions of forest dynamics, and support refinement of predictions based on new data and improved ecological understanding. A major challenge to the application of DSTMs in applied forest ecology has been the lack of a scalable, theoretical model of forest dynamics that generates predictions at the stand level—the scale at which management decisions are made. We address this challenge by integrating a matrix projection model motivated by the well-known McKendrick-von Foerster partial differential equation for size-structured population dynamics within a Bayesian hierarchical DSTM informed by continuous forest inventory data. The model provides probabilistic predictions of species-specific demographic rates and changes in the size-species distribution over time. The model is applied to predict long-term dynamics (60+ years) within the Penobscot Experimental Forest in Maine, USA, quantifying and partitioning uncertainty in inventory observations, process-based predictions, and model parameters for nine Acadian Forest species. We find that uncertainty in inventory observations drives variability in predictions for most species and limits the inclusion of ecological detail within the DSTM. We conclude with a discussion of how DSTMs can be used to reduce uncertainty in predictions of forest dynamics under global change through informed model refinement and the assimilation of multiple forest data sources.
Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum
Authors: Shrestha Nikee , Mangal Harshita , Torres-Rodriguez J. Vladimir , Tross Michael C. , Lopez-Corona Lina , Linders Kyle , Sun Guangchao , Mural Ravi V. , Schnable James C. 
Published: July 2024

AbstractSeed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes – colored metabolites in sorghum seeds – and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.
Sequence programmable nucleic acid coacervates
Authors: Majumder Sumit , Coupe Sebastian , Fakhri Nikta , Jain Ankur 
Published: July 2024

AbstractNature uses bottom-up self-assembly to build structures with remarkable complexity and functionality. Understanding how molecular-scale interactions translate to macroscopic properties remains a major challenge and requires systems that effectively bridge these two scales. Here, we generate DNA and RNA liquids with exquisite programmability in their material properties. Nucleic acids are negatively charged, and in the presence of polycations, they may condense to a liquid-like state. Within these liquids, DNA and RNA retain sequence-specific hybridization abilities. We show that intermolecular hybridization in the condensed phase cross-links molecules and slows down chain dynamics. This reduced chain mobility is mirrored in the macroscopic properties of the condensates. Molecular diffusivity and material viscosity scale with the intermolecular hybridization energy, enabling precise sequence-based modulation of condensate properties over orders of magnitude. Our work offers a robust platform to create self-assembling programmable fluids and may help advance our understanding of liquid-like compartments in cells.
Constructing Ensemble Gene Functional Networks Capturing Tissue/condition-specific Co-expression from Unlabled Transcriptomic Data with TEA-GCN
Authors: Lim Peng Ken , Wang Ruoxi , Velankanni Jenet Princy Antony , Mutwil Marek 
Published: July 2024

AbstractGene co-expression networks (GCNs) generated from public transcriptomic datasets can elucidate the co-regulatory and co-functional relationships between genes, making GCNs an important tool to predict gene functions. However, current GCN construction methods are sensitive to the quality of the data, and the interpretability of the identified relationships between genes is still difficult. To address this, we present a novel method — Two-Tier Ensemble Aggregation (TEA-) GCN. TEA-GCN utilizes unsupervised partitioning of big transcriptomic datasets and three correlation coefficients to generate ensemble GCNs in a two-step aggregation process. We show that TEA-GCN outperforms in finding correct functional relationships between genes over the current state-of-the-art across three model species, and is able to not only capture condition/tissue-specific gene co-expression but explain them through the use of natural language processing (NLP). In addition, we found TEA-GCN to be especially performant in identifying relationships between transcription factors and their activation targets, making it effective in inferring gene regulatory networks. TEA-GCN is available athttps://github.com/pengkenlim/TEA-GCN.