bluuebunny/crossref_metadata_2025_split huggingface dataset:
The retinal RNA editome is concentrated in photoreceptor-specific genes and genetically linked to vision loss
Authors: Ansell B RE , Bonelli R , Thomas S N , Manda A , Ratnapriya R , Pinelli M , Swaroop A , diBernardo D , Banfi S , Bahlo M 
Published: June 2023

ABSTRACTBACKGROUNDConversion of adenosine in RNA to inosine by ADAR enzymes, termed ‘RNA editing’, occurs at thousands of sites across the transcriptome, and is required for healthy development of the central nervous system. RNA editing can modify protein sequences, and dampen the innate immune response. RNA editing is tissue-specific and partly genetically determined. Modifications of RNA editing sites contribute to multiple diseases, particularly neurodevelopmental and neuropsychiatric diseases. Despite the importance of RNA editing in the brain, nothing is known about this process in the human retina. We describe the landscape of retinal editing revealing its importance in key biological processes that underpin vision.METHODS & RESULTSWe analysed the transcriptomes of >500 donor retinae and identified ∼153,000 high-confidence RNA editing sites. Some 80% of editing sites occurred within protein-coding RNA, with the majority in intronicAlurepeats, and 3’ UTR sequence. Novel retina-specific sites were concentrated in genes related to photoreceptor function and which cause retinitis pigmentosa, most notably in PDE6A. Exonic, protein recoding sites were enriched in zinc-finger domains. AMD subjects exhibit relatively few differences in RNA editing compared to controls, consistent with limited gene expression differences. We identified ∼10,000 editing QTLs. The genetic architecture of editing in the retina resembles the brain, whereas editing and expression QTLs in the retina show modest genetic overlap. We report colocalization between edQTLs and retinal disease GWAS peaks for age-related macular degeneration, glaucoma and macular telangiectasia. These findings provide new insights into epi-transcriptomic regulation of genes critical for vision, and elaborate putative genetic disease driver mechanisms that appear to be independent of changes in gene expression.
Transcriptomic Enrichment of Ferroptosis-Related GeneACSL4in Advanced Hepatic Fibrosis/Cirrhosis: Bioinformatics Analysis and Experimental validation
Authors: Zhang Shuya , Liu Ying , Chen Liping , Liu Yuxuan , Guo Yuqi , Cheng Jilin , Huang Jun 
Published: June 2023

AbstractBackgroundLiver fibrosis is a critical part of the clinical process of liver disease that progresses to cirrhosis and even liver cancer, and effective treatment and clinical biomarkers are urgently needed to manage liver fibrosis. Ferroptosis, a notable biological phenomenon that has received attention because of the role it performs in liver fibrosis. The objective of this research is in order to identify key ferroptosis genes related to advanced liver fibrosis/cirrhosis.MethodsGene expression differences were analyzed in liver fibrosis liver tissue of hepatitis B virus(HBV)infection patients, non-alcoholic steatohepatitis (NASH) patients and alcoholic hepatitis (AH) patients to obtain overlapping ferroptosis-related genes that are significantly up-regulated. A multigroup comparison was performed to evaluate the diagnostic clinical importance of ferroptosis-related genes of patients in differential degrees of liver fibrosis, and confirmed via gene expression trend analysis.The differential expression of candidate ferroptosis-related genes through classical carbon tetrachloride (CCl4) induced advanced liver fibrosis mice model were validated by real-time quantitative PCR (qPCR). Correlation analysis was conducted to tentatively identify the connections between hepatic ferroptosis-related genes and key genes participating in functional pathways relevant to liver fibrosis.ResultsWe screened and obtained 10 genes related to ferroptosis, all of which were significantly up-regulated in liver tissue from liver fibrosis patients of different etiologies, and identified acyl-CoA synthetase long chain family member 4 (ACSL4) was transcriptomic enriched in patients with HBV infection, NASH, AH-associated advanced liver fibrosis and cirrhotic tissue adjacent to hepatocellular carcinoma (HCC). In CCl4induced advanced liver fibrosis mice model, the hepaticACSL4expression was significantly up-regulated when compared to normal controls. In our study, we also suggest a significant association betweenACSL4and representative genes in liver fibrosis-related pathway.ConclusionWe found thatACSL4mRNA can effectively differentiate the severity of liver fibrosis, suggesting its potential clinical diagnostic value in patients with liver fibrosis regardless of its etiology.ACSL4may be a promising biomarker, which deserves further research.
Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans
Authors: Ratnasiri Kalani , Zheng Hong , Toh Jiaying , Yao Zhiyuan , Duran Veronica , Donato Michele , Roederer Mario , Kamath Megha , Todd John-Paul M. , Gagne Matthew , Foulds Kathryn E. , Francica Joseph R. , Corbett Kizzmekia S. , Douek Daniel C. , Seder Robert A. , Einav Shirit , Blish Catherine A. , Khatri Purvesh 
Published: June 2023

ABSTRACTViral pandemics and epidemics pose a significant global threat, with emerging and re-emerging viruses responsible for four pandemics in the 21st century alone. While macaques have been utilized as a model for understanding viral disease in a controlled setting, it remains unclear how conserved the antiviral responses to diverse viruses are between macaques and humans. To address this critical knowledge gap, we conducted a comprehensive cross-species analysis of transcriptomic data from over 6000 blood samples from macaques and humans infected with one of 31 viruses, including Lassa, Ebola, Marburg, Zika, and dengue. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses which are consistent regardless of infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Moreover, by leveraging longitudinal data from experimental challenges, we identified virus-specific response dynamics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Additionally, through comparative analysis of immune responses across viruses, we identified a unique enrichment of lymphoid cellular response modules in macaque Flaviviridae infection that persists in human responses to dengue. Our results underscore macaque studies as a powerful tool for gaining new insights into viral pathogenesis and immune responses that translate to humans, which can inform viral therapeutic development and enable pandemic preparedness.One sentence summaryUsing longitudinal macaque viral challenge studies, we identified shared and virus-specific responses to infection that replicate in human viral disease - thereby demonstrating the utility of macaque models of viral infection to understand antiviral biology and for pandemic preparedness.
Weak interactions cause poor performance of common network inference models
Authors: Sadykova Dinara , Yearsley Jon M. , Aderhold Andrej , Dondelinger Frank , White Hannah J. , Sanchez Lupe Leon , Ilić Maja , Sadykov Alexander , Emmerson Mark , Caplat Paul 
Published: June 2023

AbstractNetwork inference models have been widely applied in ecological, genetic and social studies to infer unknown interactions. However, little is known about how well the models perform and whether they produce reliable results when confronted with networks where weak interactions predominate and for different amounts of data. This is an important consideration as empirical interaction strengths are commonly skewed towards weaker interactions, which is especially relevant in ecological networks, and a number of studies suggest the importance of weak interactions for ensuring the dynamic stability of a system.Here we investigate four commonly used network methods (Bayesian Networks, Graphical Gaussian Models, L1-regularised regression with the least absolute shrinkage and selection operator, and Sparse Bayesian Regression) and employ network simulations with different interaction strengths to assess their accuracy and reliability.The results show poor performance, in terms of the ability to discriminate between existing relationships and no relationships, in the presence of weak interactions, for all the selected network inference methods.Our findings suggest that though these models have some promise for network inference with networks that consist of medium or strong interactions and larger amounts of data, data with weak interactions does not provide enough information for the models to reliably identify interactions. Therefore, networks inferred from data of that type should be interpreted with caution.
A mean-field to capture asynchronous irregular dynamics of conductance-based networks of adaptive quadratic integrate-and-fire neuron models
Authors: Alexandersen Christoffer G. , Duprat Chloé , Ezzati Aitakin , Houzelstein Pierre , Ledoux Ambre , Liu Yuhong , Saghir Sandra , Destexhe Alain , Tesler Federico , Depannemaecker Damien 
Published: June 2023

AbstractMean-field models are a class of models used in computational neuroscience to study the behaviour of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behaviour of “mean field” variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semi-analytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.
DeepRank-GNN-esm: A Graph Neural Network for Scoring Protein-Protein Models using Protein Language Model
Authors: Xu X. , Bonvin A. M. J. J. 
Published: June 2023

AbstractMotivationProtein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the three-dimensional structures of the correspond protein complexes valuable insights can be obtained, providing, for example, starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSM), which are computationally expensive to generate and significantly limit the algorithm’s usability.ResultsWe introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the EMS-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes).Availability and implementationDeepRank-GNN-esm is freely available fromhttps://github.com/DeepRank/DeepRank-GNN-esm
Fine-tuning Protein Embeddings for Generalizable Annotation Propagation
Authors: Dickson Andrew M. , Mofrad Mohammad R. K. 
Published: June 2023

A central goal of bioinformatics research is to understand proteins on a functional level, typically by extrapolating from experimental results with the protein sequence information. One strategy is to assume that proteins with similar sequences will also share function. This has the benefit of being interpretable; it gives a very clear idea of why a protein might have a particular function by comparing with the most similar reference example. However, direct machine learning classifiers now outperform pure sequence similarity methods in raw prediction ability. A hybrid method is to use pre-trained language models to create protein embeddings, and then indirectly predict protein function using their relative similarity. We find that fine-tuning an auxiliary objective on protein function indirectly improves these hy-brid methods, to the point that they are in some cases better than direct classifiers. Our empirical results demonstrate that interpretable protein comparison models can be developed using fine-tuning techniques, without cost, or even with some benefit, to overall performance. K-nearest neighbors (KNN) embedding-based models also offer free generalization to previously unknown classes, while continuing to outperform only pre-trained models, further demonstrating the potential of fine-tuned embeddings outside of direct classification.Significance StatementFor the purposes of biological understanding, or engineering, it is particularly important that protein annotation is done through interpretable means, such as comparison to existing proteins with known properties. Our work builds upon previous efforts to do so by embedding proteins into high-dimensional vectors with pre-trained protein language models. We demonstrate that further training of these models on protein function classification drastically improves their performance, while allowing for exactly the same similarity based classifications on both known and novel potential functions.
Rocket-miR, a Translational Launchpad for miRNA-based Antimicrobial Drug Development
Authors: Neff Samuel L. , Hampton Thomas H. , Koeppen Katja , Sarkar Sharanya , Latario Casey J. , Ross Benjamin D. , Stanton Bruce A. 
Published: June 2023

AbstractDeveloping software tools that leverage biological datasets to accelerate drug discovery is an important aspect of bioinformatics research. Here we present a novel example: a web application called Rocket-miR that applies an existing bioinformatics algorithm (IntaRNA) to predict cross-species miRNA-mRNA interactions and identify human miRNAs with potential antimicrobial activity against antibiotic resistant bacterial infections. Rocket-miR is the logical extension of our prior finding that human miRNA let-7b-5p impairs the ability of the ubiquitous opportunistic pathogenP. aeruginosato form biofilms and resist the bactericidal effect of beta lactam antibiotics. Rocket-miR’s point and click interface enables researchers without programming expertise to predict additional human-miRNA-pathogen interactions. Identified miRNAs can be developed into novel antimicrobials effective against the 24 clinically relevant pathogens, implicated in diseases of the lung, gut and other organs, that are included in the application. The manuscript incorporates three case studies contributed by microbiologists that study human pathogens to demonstrate the usefulness and usability of the application. Rocket-miR is accessible at the following link:http://scangeo.dartmouth.edu/RocketmiR/.ImportanceAntimicrobial resistant infections contribute to millions of deaths worldwide every year. In particular, the group of bacteria collectively known as ESKAPE (Enterococcus faecium,Staphylococcus aureus,Klebsiella pneumoniae,Acinetobacter baumannii,Pseudomonas aeruginosa andEnterobacter sp.)pathogens are of considerable medical concern due to their virulence and exceptional ability to develop antibiotic resistance. New kinds of antimicrobial therapies are urgently needed to treat patients for whom existing antibiotics are ineffective. The Rocket-miR application predicts targets of human miRNAs in bacterial and fungal pathogens, rapidly identifying candidate miRNA-based antimicrobials. The application’s target audience is microbiologists that have the laboratory resources to test the application’s predictions. The Rocket-miR application currently supports 24 recognized human pathogens that are relevant to numerous diseases including CF, COPD, urinary tract infections and pneumonia. Furthermore, the application code was designed to be easily extendible to other human pathogens that commonly cause hospital-acquired infections.
Pollen thermotolerance of a widespread plant in response to climate warming: possible local adaptation of populations from different elevations
Authors: Hrubá Karolína , Biella Paolo , Klečka Jan 
Published: June 2023

AbstractOne of the most vulnerable phases in the plant life cycle is sexual reproduction, which depends on effective pollen transfer, but also on the thermotolerance of pollen grains. Pollen thermotolerance is temperature-dependent and may be reduced by increasing temperature associated with global warming. A growing body of research has focused on the effect of increased temperature on pollen thermotolerance in crops to understand the possible impact of temperature extremes on yield. Yet, little is known about the effects of temperature on pollen thermotolerance of wild plant species. To fill this gap, we selectedLotus corniculatuss.l. (Fabaceae), a species common to many European habitats and conducted laboratory experiments to test its pollen thermotolerance in response to artificial increase in temperature. To test for possible local adaptation of pollen thermal tolerance, we compared data from six lowland (389 - 451 m a.s.l.) and six highland (841 - 1030 m a.s.l.) populations. We observed pollen germinationin vitroat 15, 25, 30, and 40°C. While lowland plants had stable germination rate at a wide range of temperatures between 15 and 30°C, with reduced germination rate observed only at extremely high temperatures (i.e., 40°C), the germination rate of highland plants was reduced already when the temperature reached 30°C, temperature commonly exceeded in the lowland during warm summers. This suggests that lowland populations ofL. corniculatusmay be locally adapted to higher temperature for pollen germination. On the other hand, pollen tube length decreased with increasing temperature in a similar way in lowland and highland plants. The overall average pollen germination rate significantly differed between lowland and highland populations, with highland populations displaying higher germination rate. On the other hand, the average pollen tube length was slightly smaller in highland populations. In conclusion, we found that pollen thermotolerance ofL. corniculatusis reduced at high temperature and that the germination of pollen from plant populations growing at higher elevations is more sensitive to increased temperature, which suggests possible local adaptation of pollen thermotolerance.
The universal suppressor mutation in the HSV-1 nuclear egress complex restores membrane budding defects by stabilizing the oligomeric lattice
Authors: Draganova Elizabeth B. , Wang Hui , Wu Melanie , Liao Shiqing , Vu Amber , Gonzalez-Del Pino Gonzalo L. , Zhou Z. Hong , Roller Richard J. , Heldwein Ekaterina E. 
Published: June 2023

ABSTRACTNuclear egress is an essential process in herpesviral replication whereby nascent capsids translocate from the nucleus to the cytoplasm. This initial step of nuclear egress – budding at the inner nuclear membrane – is coordinated by the nuclear egress complex (NEC). Composed of the viral proteins UL31 and UL34, NEC deforms the membrane around the capsid as the latter buds into the perinuclear space. NEC oligomerization into a hexagonal membrane-bound lattice is essential for budding because mutations designed to perturb lattice interfaces reduce its budding ability. Previously, we identified an NEC suppressor mutation capable of restoring budding to a mutant with a weakened hexagonal lattice. Here, we show that the suppressor mutation can restore budding to a broad range of budding-deficient NEC mutants thereby acting as a universal suppressor. We demonstrate that the suppressor mutation indirectly promotes the formation of new contacts between the NEC hexamers that, ostensibly, stabilize the hexagonal lattice. This stabilization strategy is powerful enough to override the otherwise deleterious effects of mutations that destabilize the NEC lattice by different mechanisms, resulting in a functional NEC hexagonal lattice and restoration of membrane budding.
Opto-seq reveals input-specific immediate early gene induction in ventral tegmental area cell types
Authors: Simon Rhiana C. , Loveless Mary C. , Yee Joshua X. , Hashikawa Koichi , Stuber Garret D. , Zweifel Larry S. , Soden Marta E. 
Published: June 2023

SummaryThe ventral tegmental area (VTA) is a critical node in circuits governing motivated behavior and is home to diverse populations of neurons that release dopamine, GABA, glutamate, or combinations of these neurotransmitters. The VTA receives inputs from many brain regions, but a comprehensive understanding of input-specific activation of VTA neuronal subpopulations is lacking. To address this, we combined optogenetic stimulation of select VTA inputs with single-nucleus RNA sequencing (snRNAseq) and highly multiplexedin situhybridization to identify distinct neuronal clusters and characterize their spatial distribution and activation patterns. Quantification of immediate early gene (IEG) expression revealed that different inputs activated select VTA subpopulations, which demonstrated cell-type specific IEG programs. Within dopaminergic subpopulations IEG induction levels correlated with differential expression of ion channel genes. This new transcriptomics-guided circuit analysis reveals the diversity of VTA activation driven by distinct inputs and provides a resource for future analysis of VTA cell types.
DNA binding activity of CAMTA3 is essential for its function: Identification of critical amino acids for its transcriptional activity
Authors: Prasad Kasavajhala V.S.K. , Abdel-Hameed Amira , Jiang Qiyan , Reddy Anireddy S.N. 
Published: June 2023

SummaryCalmodulin-binding transcription activators (CAMTAs), a small family of highly conserved transcription factors, function in calcium-mediated signaling pathways. Of the six CAMTAs in Arabidopsis, CAMTA3 (also referred to as SR1) regulates diverse biotic and abiotic stress responses. A recent study has shown that CAMTA3 is a guardee of NLR ((nucleotide-binding, leucine-rich repeat domain) immune receptors in modulating plant immunity, thereby suggesting that CAMTA3 transcriptional activity is not necessary for its function. Here, we show that the DNA-binding activity of CAMTA3 is essential for its role in mediating plant immune responses. Analysis of the DNA binding (CG-1) domain of CAMTAs in plants and animals showed strong conservation of several amino acids. We mutated several conserved amino acids in the CG-1 domain to investigate their role in CAMTA3 function. Electrophoretic mobility shift assays using these mutants with a promoter of its target gene identified critical amino acid residues necessary for DNA binding activity. Furthermore, transient assays showed that these residues are essential for the CAMTA3 function in activating theRSRE(Rapid Stress Response Element)-driven reporter gene expression. In line with this, transgenic lines expressing the CG-1 mutants of CAMTA3 in thecamta3mutant failed to rescue the mutant phenotype and restore the expression of CAMTA3 downstream target genes. Collectively, our results provide biochemical and genetic evidence that the transcriptional activity of CAMTA3 is indispensable for its function.