bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tau P301L mutation promotes core 4R tauopathy fibril fold through near-surface water structuring and conformational rearrangement
Authors: Vigers Michael P. , Lobo Samuel , Najafi Saeed , Dubose Austin , Tsay Karen , Ganguly Pritam , Longhini Andrew P. , Jin Yingying , Buratto Steven K. , Kosik Kenneth S. , Shell M. Scott , Shea Joan-Emma , Han Songi 
Published: November 2023

AbstractTau forms toxic fibrillar aggregates in a family of neurodegenerative diseases known as tauopathies. The faithful replication of tauopathy-specific fibril structures is a critical gap for developing diagnostic and therapeutic tools. This study debuts a strategy of identifying a critical segment of tau that forms a folding motif that is characteristic of a family of tauopathies and isolating it as a standalone peptide that form seeding-competent fibrils. The 19-residue jR2R3 peptide (295-313) spanning the R2/R3 splice junction of tau, in the presence of P301L, forms seeding-competent amyloid fibrils. This tau fragment contains the hydrophobic VQIVYK hexapeptide that is part of the core of every pathological tau fibril structure solved to-date and an intramolecular counter-strand that stabilizes the strand-loop-strand (SLS) motif observed in 4R tauopathy fibrils. This study shows that P301L exhibits a duality of effects: it lowers the barrier for the peptide to adopt aggregation-prone conformations and enhances the local structuring of water around the mutation site that facilitates site-specific dewetting and in-register stacking of tau to form cross β-sheets. We solve a 3 Å cryo-EM structure of jR2R3-P301L fibrils with a pseudo 21screw symmetry in which each half of the fibril’s cross-section contains two jR2R3-P301L peptides. One chain adopts a SLS fold found in 4R tauopathies that is stabilized by a second chain wrapping around the SLS fold, reminiscent of the 3-fold and 4-fold structures observed in 4R tauopathies. These jR2R3-P301L fibrils are able to template full length tau in a prion-like fashion.Significance StatementThis study presents a first step towards designing a tauopathy specific aggregation pathway by engineering a minimal tau prion building block, jR2R3, that can template and propagate distinct disease folds. We present the discovery that P301L—among the widest used mutations in cell and animal models of Alzheimer’s Disease—destabilizes an aggregation-prohibiting internal hairpin and enhances the local surface water structure that serves as an entropic hotspot to exert a hyper-localized effect in jR2R3. Our study suggests that P301L may be a more suitable mutation to include in modeling 4R tauopathies than for modelling Alzheimer’s Disease, and that mutations are powerful tools for the purpose of designing of tau prion models as therapeutic tools.
Genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Swiss Large White pigs
Authors: Ewaoluwagbemiga Esther Oluwada , Lloret-Villas Audald , Nosková Adéla , Pausch Hubert , Kasper Claudia 
Published: November 2023

AbstractBackgroundThe improvement of protein efficiency (PE) is a key factor for a sustainable pig production as nitrogen excretion contributes substantially to environmental pollution. Protein efficiency has been shown to be clearly heritable and genetically correlated with some performance traits, such as feed conversion ratio (FCR) and average daily feed intake (ADFI). This study aimed to identify genomic regions associated with these traits through genome-wide association studies (GWAS) and regional heritability mapping (RHM) using whole-genome sequence variants from low-pass sequencing of more than 1,000 Swiss Large White pigs.ResultsThe genomic-based heritability estimates using ∼15 million variants were moderate to high, ranging from 0.33 to 0.47. Using GWAS, no significant variants were found at the genome-wide thresholds for PE and FCR, but 45 variants were identified for ADFI on chromosome 1and one for ADG on chromosome 14. No region was found to be significantly associated with PE and FCR using RHM. We identified five suggestive regions on chromosome 1 for ADFI and one on chromosome 14 for ADG. Combining both analyses, we were able to highlight putative candidate genes for PE, which includedPHYKPL, COL23A1, PPFIBP2, GVIN1, SYT9, RBMXL2, ZNF215and olfactory receptor genes.ConclusionsCombining GWAS and RHM allowed us to suggest genomic regions that potentially influence PE and production traits. The apparent difficulty in detecting significant regions for these traits probably reflects the relatively small sample size, differences in genetic architecture across experimental conditions, and the possibility that polymorphisms explaining large parts of the trait variation may not segregate in the population. Nevertheless, we identified plausible functional candidate genes in the highlighted regions, including those involved in nutrient sensing, the urea cycle, and metabolic pathways, in particular IGF1-insulin), and have previously been reported in association with nitrogen metabolism in cattle, muscle and adipose tissue metabolism and feed intake in pigs. We also highlighted a range of noncoding RNAs, and their targets and gene regulation in general in this context should be investigated in the future.
Exploring the Potential of Large Language Models in Molecular Tasks: An Insightful Evaluation with GPT‐4
Authors: Zhang Jinlu , Fang Yin , Zhang Ningyu , Shao Xin , Chen Huajun , Fan Xiaohui 
Published: November 2023

AbstractIn the rapidly changing realm of artificial intelligence, large language models (LLMs) such as GPT-4 are increasingly being explored for their potential to aid and enhance the field of molecular research. This study explores the performance of GPT-4 and GPT-3.5 in molecular research, particularly in generating and optimizing molecular structures. The results highlight GPT-4’s strengths in certain areas of molecular optimization, while also revealing challenges in accurately generating complex molecules. The findings underscore the necessity for integrating these models with domain-specific tools to enhance their application in scientific research, particularly in molecular studies. The study offers insights into the potential of LLMs for advancing molecular research, paving the way for future developments in this rapidly evolving field.
A library of reporters of the global regulators of gene expression ofEscherichia coli
Authors: Dash Suchintak , Jagadeesan Rahul , Baptista Ines S.C. , Chauhan Vatsala , Kandavalli Vinodh , Oliveira Samuel M.D. , Ribeiro Andre S. 
Published: November 2023

SUMMARYThe topology of the transcription factor network (TFN) ofE. coliis far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs for this purpose. Each consists of a single-copy plasmid coding for GFP fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, as well as under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs ofE. coli. Moreover, the library can be invaluable in bioindustrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.Abstract Figure
ChemXTree:A Tree-enhanced Classification Approach to Small-molecule Drug Discovery
Authors: Xu Yuzhi , Liu Xinxin , Ge Jiankai , Xia Wei , Ju Cheng-Wei , Zhang Haiping , Zhang John Z.H. 
Published: November 2023

AbstractThe rapid advancement of machine learning, particularly deep learning, has propelled significant strides in drug discovery, offering novel methodologies for molecular property prediction. However, despite these advancements, existing approaches often face challenges in effectively extracting and selecting relevant features from molecular data, which is crucial for accurate predictions. Our work introduces ChemXTree, a novel graph-based model that integrates tree-based algorithms to address these challenges. By incorporating a Gate Modulation Feature Unit (GMFU) for refined feature selection and a differentiable decision tree in the output layer. Extensive evaluations on benchmark datasets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree’s superior performance, particularly in feature optimization. Permutation experiments and ablation studies further validate the effectiveness of GMFU, positioning ChemXTree as a significant advancement in molecular informatics, capable of rivaling state-of-the-art models.
Population coding of distinct categories of behavior in the frontal eye field
Authors: Cain Matan , Joshua Mati 
Published: November 2023

AbstractBrain regions frequently contribute to the control of diverse behaviors. Within these regions, individual neurons often respond to various motor tasks, suggesting that the ability to control multiple behaviors is rooted in the organization of neuronal populations. To study this organization, we examined how the frontal eye field (FEF) encodes distinct behaviors by recording the activity of 1200 neurons during various eye movement tasks in two female fascicularis monkeys. We focused on three behaviors: smooth pursuit, pursuit suppression, and saccades. We found that single neurons tended to respond in all three tasks, thus challenging the notion that the FEF is organized into task-specific clusters. We then identified the low-dimensional subspaces that contained most of the population activity during each behavior and quantified the extent of overlap between these spaces across behaviors. Population activity during pursuit and pursuit suppression exhibited a substantial overlap, with highly correlated directional tuning at the single-neuron level, as reflected by similar outcomes for the population decoders. These distinct behaviors combined with similar encoding suggest that the suppression of movement occurs mostly downstream from the FEF. By contrast, pursuit and saccades mostly occupied orthogonal subspaces, prompting an independent linear readout of saccades and pursuit. Thus overall, these results indicate that distinct behaviors can exhibit either separate or overlapping population codes within a specific brain region, hence emphasizing the importance of the system-level organization of behavior.Significance statementHow do brain areas control multiple behaviors? We investigated the organization of the FEF in monkeys during three different types of eye movements: smooth pursuit, pursuit suppression, and saccades. We found that individual neurons tended to respond to all three tasks, thus challenging a task-specific FEF cluster organization. Further, the low-dimensional subspaces that contained most of the population activity during pursuit and pursuit suppression overlapped substantially, implying that movement suppression occurs downstream from the FEF. In contrast, pursuit and saccades occupied orthogonal subspaces, prompting independent linear readouts. These results underscore the importance of adopting a system-level perspective to comprehend how diverse behaviors are encoded in the brain.
In vivodetection of DNA secondary structures using Permanganate/S1 Footprinting with Direct Adapter Ligation and Sequencing (PDAL-Seq)
Authors: Lahnsteiner Angelika , Craig Sarah J. C. , Kamali Kaivan , Weissensteiner Bernadette , McGrath Barbara , Risch Angela , Makova Kateryna D. 
Published: November 2023

AbstractDNA secondary structures are essential elements of the genomic landscape, playing a critical role in regulating various cellular processes. These structures refer to G-quadruplexes, cruciforms, Z-DNA or H-DNA structures, amongst others (collectively called ‘non-B DN’), which DNA molecules can adopt beyond the B conformation. DNA secondary structures have significant biological roles, and their landscape is dynamic and can rearrange due to various factors, including changes in cellular conditions, temperature, and DNA-binding proteins. Understanding this dynamic nature is crucial for unraveling their functions in cellular processes. Detecting DNA secondary structures remains a challenge. Conventional methods, such as gel electrophoresis and chemical probing, have limitations in terms of sensitivity and specificity. Emerging techniques, including next-generation sequencing and single-molecule approaches, offer promise but face challenges since these techniques are mostly limited to only one type of secondary structure. Here we describe an updated version of a technique permanganate/S1 nuclease footprinting, which uses potassium permanganate to trap single-stranded DNA regions as found in non-B structures, in combination with S1 nuclease digest and adapter ligation to detect genome-wide non-B formation. To overcome technical hurdles, we combined this method with direct adapter ligation and sequencing (PDAL-Seq). Furthermore, we established a user-friendly pipeline available on Galaxy to standardize PDAL-Seq data analysis. This optimized method allows the analysis of many types of DNA secondary structures that form in a living cell and will advance our knowledge of their roles in health and disease.
Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection
Authors: Liu Hongwei , Zhang Wei , Zhang Yihao , Adegboro Abraham Ayodeji , Dai Luohuan , Pan Zhouyang , Li Wang , Peng Kang , Fasoranti Deborah Oluwatosin , Wanggou Siyi , Li Xuejun 
Published: November 2023

ABSTRACTWith the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patient’s outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.
Analysis ofE. coligrowth dynamics during Lambda(vir) phage infection reveals phage decay
Authors: Gitai Benjamin M. , Sheehan Joseph P. 
Published: November 2023

AbstractBiological interactions like those between a pathogen and a host are intrinsically dynamic. The Luria-Delbruck experiment is a classic quantitative analysis demonstrating that the mutation rates of bacterial hosts are independent of the presence of their bacteriophage pathogens. However, this experiment was performed as an endpoint analysis. To determine if new insights could be gained from measuring the dynamics of bacterial-host interactions, we used a high-throughput spectrophotometer to repeat this classic experiment. We examined interactions betweenEscherichia colibacteria and the lytic lambda(vir) phage to confirm the fundamental finding of the Luria-Delbruck experiment. Specifically, we demonstrated that the variance in the timing required for phage-resistant mutants to emerge increased as the size of the starting bacterial population decreased. In addition to confirming the Luria-Delbruck results, our analysis of the dynamics of bacterial growth in the presence of phage revealed unexpected findings, including the significant decay in lambda(vir) potency and the substantial impact of dead bacterial debris on culture density measurements. Phage are typically thought to be extremely stable, such that our discovery that their potency rapidly decays in experimental settings may have implications for real-world applications where phage potency is important, such as phage therapy pharmacology. Our results thus establish a more rapid method of analyzing bacterial resistant mutant fluctuations and illustrate how dynamical measurements can provide new insights into even extremely well-characterized experimental systems.
Topological Learning Approach to Characterizing Biological Membranes
Authors: Arango Andres S. , Park Hyun , Tajkhorshid Emad 
Published: November 2023

AbstractBiological membranes play key roles in cellular compartmentalization, structure, and its signaling pathways. At varying temperatures, individual membrane lipids sample from different configurations, a process that frequently leads to higher-order phase behavior and phenomena. Here we present a persistent homology-based method for quantifying the structural features of individual and bulk lipids, providing local and contextual information on lipid tail organization. Our method leverages the mathematical machinery of algebraic topology and machine learning to infer temperature-dependent structural information of lipids from static coordinates. To train our model, we generated multiple molecular dynamics trajectories of DPPC membranes at varying temperatures. A fingerprint was then constructed for each set of lipid coordinates by a persistent homology filtration, in which interactions spheres were grown around the lipid atoms while tracking their intersections. The sphere filtration formed asimplicial complexthat captures enduring keytopological featuresof the configuration landscape, using homology, yieldingpersistence data. Following fingerprint extraction for physiologically relevant temperatures, the persistence data were used to train an attention-based neural network for assignment of effective temperature values to selected membrane regions. Our persistence homology-based method captures the local structural effects, via effective temperature, of lipids adjacent to other membrane constituents, e.g. sterols and proteins. This topological learning approach can predict lipid effective temperatures from static coordinates across multiple spatial resolutions. The tool, calledMembTDA, can be accessed athttps://github.com/hyunp2/Memb-TDA.
Morphometrics and phylogenomics of coca (Erythroxylumspp.) illuminate its reticulate evolution, with implications for taxonomy
Authors: Przelomska Natalia A. S. , Diaz Rudy A. , Ávila Fabio Andrés , Ballen Gustavo A. , Cortés-B Rocío , Kistler Logan , Chitwood Daniel H. , Charitonidou Martha , Renner Susanne S. , Pérez-Escobar Oscar A. , Antonelli Alexandre 
Published: November 2023

AbstractSouth American coca (Erythroxylum cocaandE. novogranatense) has been a key-stone crop for many Andean and Amazonian communities for at least 8,000 years. However, over the last half century, global demand for cocaine has placed this plant in the centre of armed conflict, deforestation, and explosive growth of illegal economies. While national and international agencies progress from a ‘war on drugs’ policy model towards locally appropriate, data-informed strategies to tackle coca plantations, monitoring their expansion and composition remains essential. The principal means to identify coca plants is leaf morphology, yet the extent to which it is reflected in taxonomy is uncertain. Here, we analyse the consistency of the current naming system of coca and its four closest wild relatives (the ‘coca clade’), using morphometrics, phylogenomics, and population genomics. We include the name-bearing type specimens of coca’s closest wild relativesE. gracilipesandE. cataractarum. Morphometrics of 342 digitized herbarium specimens show that leaf shape and size fail to reliably discriminate between species and varieties. However, the rounder and more obovate leaves of certain coca varieties could be associated with domestication syndrome of this crop. Our phylogenomic data indicate gene flow involving monophyletic clades ofE. gracilipesand theE. cocaclade. These results further clarify the evolution of coca and support a taxonomic framework whereinE. gracilipesis retained as a single species. Our findings have implications for the development of cost-effective genotyping methods to effectively discriminate varieties of cultural significance from high-yielding cultivars fuelling the lucrative cocaine market.
BCG vaccination impacts the epigenetic landscape of progenitor cells in human bone marrow
Authors: Sun Sarah J. , Aguirre-Gamboa Raúl , de Bree L. Charlotte J. , Sanz Joaquin , Dumaine Anne , Joosten Leo A.B. , Divangahi Maziar , Netea Mihai G. , Barreiro Luis B. 
Published: November 2023

SUMMARYWhile the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo administration. We show that BCG vaccination significantly alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occur primarily on the most uncommitted stem cells and are reflective of a persistent myeloid bias. In contrast, BCG-induced changes in chromatin accessibility are most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF)/SP and EGR transcription factors (TFs). These TFs are also activated in the most uncommitted stem cells, indicating that activated TFs, which drive persistent changes in HSC gene expression, likely also drive chromatin dynamics appearing within downstream progenitor cells. This perspective contests the prevailing notion that epigenetic modifications linked to innate immune memory transfer directly from stem cells to their differentiated derivatives. Finally, we show that alterations in gene expression and chromatin accessibility in HSPCs due to BCG vaccination were highly correlated (r>0.8) with the IL-1β secretion capacity of paired PBMCs upon secondary immune challenge. Overall, our findings shed light on BCG vaccination’s profound and lasting effects on HSPCs and its influence on innate immune responses.