bluuebunny/crossref_metadata_2025_split huggingface dataset:
CONSTRUCT: an algorithmic tool for identifying functional or structurally important regions in protein tertiary structure
Authors: Chivot Lucas , Bridier-Nahmias Antoine , Favennec Loïc , Gelly Jean-Christophe , Clain Jérôme , Coppée Romain 
Published: October 2024

AbstractIn the realm of protein-coding genes, evolutionary rates show considerable variability. Essential or highly expressed proteins evolve more slowly, and within a protein, different amino acid sites evolve at different rates. Accurately modeling this variation is critical for identifying structurally or functionally important amino acid sites. Standard methods such as Rate4Site assume independent substitution rates across sites, and the most conserved ones are widely distributed in protein tertiary structure. This is biologically unrealistic because functional sites often cluster together. Here, we developed CONSTRUCT, an improved strategy for identifying functional and structurally important regions in protein tertiary structure. Given a set of orthologous sequences, CONSTRUCT first calculates site-specific substitution rates using the Rate4site model, which are then weighted by the rates of neighboring amino acid sites within a range of window sizes. The optimal window size is determined by the strongest spatial correlation, if present. This method can be analyzed using either Cα atoms or the center of mass of amino acid sites to account for side chain orientation. Validation on 14 functionally characterized proteins of different size, conservation level and kingdom/domain of species has demonstrated the relevance of CONSTRUCT.
Variation in precipitation drives differences in interactions and short-term transient instability between grassland functional groups: a stage-structured community approach
Authors: Gupta Aryaman , Gascoigne Samuel , Barabás György , Qi Man , Fenollosa Erola , Thornley Rachael , Hernandez Christina , Hector Andy , Salguero-Gómez Roberto 
Published: October 2024

AbstractClimate change is expected to increase the frequency and severity of precipitation extremes, causing droughts and flooding. Consequently, grassland communities are forecasted to become increasingly unstable. To predict grassland responses, we need empirical information together with models that reliably extrapolate community dynamics from those observations. However, such prediction is challenging because community models typically simulate long-term (asymptotic) performance, and thus potentially neglect their short-term (transient) performance. Here, we use data from a precipitation experiment performed over eight years to model both short- and long-term responses of three functional groups – grasses, legumes, and non-leguminous forbs – to precipitation extremes. We use multi-functional-group Integral Projection Models and pseudospectral theory, to track grassland community dynamics. We show that the percentage-cover-stage-structure of functional groups shapes their transient instability, and that inter-functional-group interactions are competitive under increased precipitation but facilitative under decreased precipitation. IPMs and pseudospectra enable forecasting of how functional-group-stage-structure drives responses to climatic extremes.
LipiDetective a deep learning model for the identification of molecular lipid species in tandem mass spectra
Authors: Würf Vivian , Köhler Nikolai , Molnar Florian , Hahnefeld Lisa , Gurke Robert , Witting Michael , Pauling Josch K. 
Published: October 2024

AbstractLipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a “ready-to-use” application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved.
LPS binding caspase activation and recruitment domains (CARDs) are bipartite lipid binding modules
Authors: Cao Anh B. , Devant Pascal , Wang Chengliang , Sun Mengyu , Kennedy Stephanie N. , Ruan Jianbin , Kagan Jonathan C. 
Published: October 2024

AbstractCaspase-11 is an innate immune pattern recognition receptor (PRR) that detects cytosolic bacterial lipopolysaccharides (LPS) through its caspase activation and recruitment domain (CARD), triggering inflammatory cell death known as pyroptosis. Caspase-11 also detects eukaryotic (i.e.self) lipids. This observation raises the question of whether common or distinct mechanisms govern the interactions with self and nonself lipids. In this study, using biochemical, computational, and cell-based assays, we report that the caspase-11 CARD functions as a bipartite lipid-binding module. Distinct regions within the CARD bind to phosphate groups and long acyl chains of self and nonself lipids. Self-lipid binding capability is conserved across numerous caspase-11 homologs and orthologs. The symmetry in self and nonself lipid detection mechanisms enabled us to engineer an LPS-binding domainde novo, using an ancestral CARD-like domain present in the fishAmphilophus citrinellus. These findings offer critical insights into the molecular basis of LPS recognition by caspase-11 and highlight the fundamental and likely inseparable relationship between self and nonself discrimination.
Investigating Brain and Biological Development in Children and Their Relationship with Physical, Mental, and Academic Outcomes
Authors: Chang Hansoo , Street Kevin , Ferariu Ana , Taylor Alexei , Kounios John , Zhang Fengqing 
Published: October 2024

AbstractBrain age and biological age, estimated using machine learning models with brain imaging and biological features, have emerged as promising biomarkers for predicting a broad range of health outcomes in adults. However, very few studies have examined the counterpart of brain age and biological age in children, that is Brain Development Index (BRDI) and Biological Development Index (BIDI). Existing studies on BRDI and BIDI are largely cross-sectional and do not provide adequate information on their temporal trajectory and predictive power for future health outcomes in children. Additionally, the interconnectedness of BRDI and BIDI across multiple health domains, especially child-specific developmental outcomes, remains underexplored. Our study utilized brain imaging features and blood-based biomarkers from the Adolescent Brain and Child Development (ABCD) study to assess the trajectory of BRDI and BIDI over multiple time points. We examined their relationships with physical, mental, and academic health outcomes.Lastly, we utilize Bayesian network analysis to examine the relationship between the two indexes and their subcomponents. We found that delayed BRDI and BIDI were significantly associated with adverse future health outcomes across several domains. In addition, Bayesian network analysis revealed BRDI and BIDI subcomponents influence one another across different organ systems. Additionally, males exhibited more advanced BRDI, while females showed more advanced BIDI, revealing important sex differences in adolescent development. This research provides the first comprehensive analysis of BRDI and BIDI trajectories, revealing their predictive power for future health outcomes and offering new insights into the interconnected development of brain and biological systems in children.
Longitudinal multi-omics reveals pathogenicTSC2variants disrupt developmental trajectories of human cortical organoids derived from Tuberous Sclerosis Complex
Authors: Niu Weibo , Yu Shaojun , Li Xiangru , Wang Zhen , Chen Rui , Michalski Christina , Jahangiri Arman , Zohdy Youssef , Chern Joshua J , Whitworth Ted J , Wang Jianjun , Xu Jie , Zhou Ying , Qin Zhaohui , Li Bingshan , Gambello Michael J , Peng Junmin , Wen Zhexing 
Published: October 2024

ABSTRACTTuberous Sclerosis Complex (TSC), an autosomal dominant condition, is caused by heterozygous mutations in either theTSC1orTSC2genes, manifesting in systemic growth of benign tumors. In addition to brain lesions, neurologic sequelae represent the greatest morbidity in TSC patients. Investigations utilizingTSC1/2-knockout animal or human stem cell models suggest that TSC deficiency-causing hyper-activation of mTOR signaling might precipitate anomalous neurodevelopmental processes. However, how the pathogenic variants ofTSC1/2genes affect the longitudinal trajectory of human brain development remains largely unexplored. Here, we employed 3-dimensional cortical organoids derived from induced pluripotent stem cells (iPSCs) from TSC patients harboringTSC2variants, alongside organoids from age- and sex-matched healthy individuals as controls. Through comprehensively longitudinal molecular and cellular analyses of TSC organoids, we found thatTSC2pathogenic variants dysregulate neurogenesis, synaptogenesis, and gliogenesis, particularly for reactive astrogliosis. The altered developmental trajectory of TSC organoids significantly resembles the molecular signatures of neuropsychiatric disorders, including autism spectrum disorders, epilepsy, and intellectual disability. Intriguingly, single cell transcriptomic analyses on TSC organoids revealed thatTSC2pathogenic variants disrupt the neuron/reactive astrocyte crosstalk within the NLGN-NRXN signaling network. Furthermore, cellular and electrophysiological assessments of TSC cortical organoids, along with proteomic analyses of synaptosomes, demonstrated that theTSC2variants precipitate perturbations in synaptic transmission, neuronal network activity, mitochondrial translational integrity, and neurofilament formation. Notably, similar perturbations were observed in surgically resected cortical specimens from TSC patients. Collectively, our study illustrates that disease-associatedTSC2variants disrupt the neurodevelopmental trajectories through perturbations of gene regulatory networks during early cortical development, leading to mitochondrial dysfunction, aberrant neurofilament formation, impaired synaptic formation and neuronal network activity.
The utility of single-cell RNA sequencing data in predicting plant metabolic pathway genes
Authors: Ma Jingwei , Zou Liting , Wang Zhenglin , Wang Xiaoxue , Zuo Xinyi , Wang Fei , Wang Zhiqing , Li Zhimei , Li Lin , Wang Peipei 
Published: October 2024

SummaryIt is an ever challenging task to make genome-wide predictions for plant metabolic pathway genes (MPGs) encoding enzymes that catalyze the biosynthesis of plant natural products.Here, starting from 1,130 benchmark MPGs that have experimental evidence inArabidopsis thaliana, we investigate the utilities of single-cell RNA sequencing (scRNA-seq) data—a recently arisen omics data that has been used in several other fields—in predicting MPGs using four machine learning (ML) algorithms that support multi-label tasks.Compared with traditional bulk RNA-seq data, scRNA-seq data lead to different and tighter co-expression networks among MPGs within metabolic classes, but relatively lower prediction accuracy of MPGs into classes. Splitting the scRNA-seq data into tissue-specific subsets can improve the gene co-expression network tightness and prediction accuracy of MPGs for some classes. Expression features from the same tissue types in bulk RNA-seq and scRNA-seq data have different contributions to the prediction of MPGs into classes. Models built using the ensemble algorithm AutoGluon outperforms those using other three classical ML algorithms.Our results demonstrate the usefulness and characteristics of scRNA-seq data in predicting MPGs into metabolic classes, and propose that more effort is needed in the future to improve the model prediction performance.
Enhancing Uploads of Health Data in the Electronic Health Record – The Role of Framing and Length of Privacy Information
Authors: von Kalckreuth Niklas , Feufel Marks A 
Published: October 2024

AbstractBackgroundThe German electronic health record (EHR) aims to enhance patient care and reduce costs, but users often worry about data security. In this article, we propose and test communication strategies to mitigate privacy concerns and increase EHR uploads.ObjectiveWe explore whether presenting a privacy fact sheet (PFS) before interacting with the EHR increases users’ willingness to upload medical reports. Our study examines the effects of PFS framing and length on this decision.MethodsIn an online user study with 227 German participants, we used a realistic EHR click dummy and varied the PFS in terms of length (short vs. long) and framing (EHR-centered vs. patient-centered).ResultsThe results show that a PFS has a positive effect on uploading (OR 4.276, P=.015). Although there was no effect regarding the length of a PFS, a patient-centered framing increased uploads compared to an EHR-centered framing (OR 4.043,P=.003).ConclusionDisplaying PFSs at the beginning of an upload process is a cost-effective intervention to boost EHR adoption and increase diagnoses uploads. Specifically, the effectiveness of the PFS depends more on how information is framed rather than its length. Willingness to upload medical data is key to EHR success and its benefits, like better treatment and lower costs.
Anti-tumor activity of camptothecin analog conjugate of a RSPO4-based peptibody targeting LGR4/5/6 in preclinical models of colorectal cancer
Authors: Toh Yukimatsu , Wu Ling , Tu Jianghua , Liang Zhengdong , Aldana Adela M. , Wen Jake J. , Li Li , Pan Sheng , Julie Rowe H. , Hensel Martha E. , Hodo Carolyn L. , Finch Rick A. , Carmon Kendra S. , Liu Qingyun J. 
Published: October 2024

AbstractAntibody-drug conjugates (ADCs) have emerged as a major modality of targeted cancer therapy, yet no ADC has been approved specifically for colorectal cancer (CRC). LGR4/5/6 (leucine-rich repeat containing, G protein-coupled receptor 4, 5, 6) are three related receptors that are expressed at high levels together or alternately in nearly all cases of CRC. ADCs targeting LGR5 have been shown to have robust anti-tumor potency, but not all CRC cells express LGR5 and LGR5-positive tumor cells may lose LGR5 expression due to cancer cell plasticity. R-spondin 4 (RSPO4) is a natural protein ligand of LGR4/5/6 with high affinity for all three receptors. We fused a mutant form of RSPO4 furin domain that retains high affinity binding to LGR4/5/6 to human IgG1 Fc to create a peptibody designated R462. Conjugation of R462 with a camptothecin analog designated CPT2 at eight drugs per peptibody led to the generation of R462-CPT2 that showed highly potent cytotoxic activity in vitro in CRC cell lines expressing any of LG4/5/6. In cell line xenograft and PDX models of CRC, R462-CPT2 demonstrated robust anti-tumor effect. Importantly, R462-CPT2 showed no major adverse effect at therapeutically effective dose levels. These results strongly support the use of RSPO ligand drug-conjugates that target LGR4/5/6 simultaneously for the treatment of CRC.
Endogenous SNAP-tagging of Munc13-1 for monitoring synapse nanoarchitecture
Authors: Kowald Maria , Bachollet Sylvestre P. J. T. , Benseler Fritz , Steinecker Maria , Boll Moritz , Kaushik Sofia , Soykan Tolga , Sun Siqi , Birke Ramona , Ilic Dragana , Brose Nils , Hörnberg Hanna , Lehmann Martin , Rizzoli Silvio O. , Broichhagen Johannes , Lipstein Noa 
Published: October 2024

ABSTRACTSynaptic function is governed by highly regulated protein machineries, whose abundance and spatial localization change continually. Studies to determine the dynamic changes in synaptic proteins nanoarchitecture typically rely on immunolabeling, or on the expression of fluorescent proteins. The former employs chemical fluorophores and signal amplification, but requires fixation. The latter enables monitoring of proteins in live microscopy, but uses suboptimal fluorophores. Self-labeling tags have been introduced to combine the advantages of these two approaches, and here we introduce a knock-in mouse line where the essential presynaptic protein Munc13-1 is endogenously fused to the self-labeling SNAP tag. We demonstrate efficient Munc13-1-SNAP labeling in fixed neurons and brain sections by various SNAP dyes, as well as by a novel far-red and cell impermeable compound, SBG-SiR-d12. We introduce and characterize SBG-SiR-d12 here as a highly-efficient dye for SNAP-tag labeling of extracellular epitopes, and of intracellular proteins in fixed and permeabilized tissue. Finally, we show that Munc13-1-SNAP can be labeled in living neurons and monitored through live-cell imaging using confocal- and super resolution microscopy. We conclude that the Unc13aSNAPmouse line is a useful tool for the analysis of presynaptic nanoarchitectural dynamics, with a potential for wide adoption.
Multiple neural pathways to successful visual short-term memory across the adult lifespan
Authors: Jansen Michelle G. , Salami Alireza , Martínez Fernando Ruiz , Mitchell Daniel J. , Oosterman Joukje M. ,   Cam-CAN, Geerligs Linda 
Published: October 2024

AbstractCognitive task performance can be supported through multiple neural pathways, a concept referred to as brain degeneracy. We used a novel approach to consider brain degeneracy during a visual short-term memory task (VSTM) across the adult lifespan in the Cam-CAN study (n = 113, 23-87 years old). Our main goal was to identify subgroups of participants whose VSTM performance was characterized by distinct brain activation patterns. First, we identified seven brain modules that responded similarly to the VSTM task and resembled previously identified functional networks (adjusted mutual information [aMI] = 0.45). Subsequently, latent profile analysis revealed four distinct subgroups of participants. Each subgroup was characterized by different recruitment patterns of these brain modules, predominantly in the frontal control module (FCM), visual module (VM), and default mode module (DMM). Subgroups did not differ in demographics or task performance. However, associations between brain activity and performance varied across subgroups, particularly in the FCM, suggesting that individuals may use different cognitive operations to perform the VSTM task. Further analyses revealed group differences in white matter integrity, mostly in the uncinate fasciculus, suggested that individual differences in structural brain properties may shape the different brain activation patterns. Altogether, our study contributes to our understanding of how multiple neural pathways could underlie cognitive performance.
Coalescence-Driven Local Crowding Promotes Liquid-to-Solid-Like Phase Transition in a Homogeneous and Heterogeneous Droplet Assembly
Authors: Patel Chinmaya Kumar , Mallik Abhradip , Rath Deb Kumar , Kumar Rajesh , Mukherjee Tushar Kanti 
Published: October 2024

AbstractLiquid-to-solid-like phase transition (LSPT) of disordered proteinsviametastable liquid-like droplets is a well-documented phenomenon in biology and linked to many pathological conditions including neurodegenerative diseases. However, very less is known about the early microscopic events and transient intermediates involved in the irreversible protein aggregation of functional globular proteins. Herein, using a range of microscopic and spectroscopic techniques, we show that the LSPT of a functional globular protein, human serum albumin (HSA) is exclusively driven by spontaneous coalescence of liquid-like droplets involving various transient intermediates in a temporal manner. We show that inter-droplet communication via coalescence is essential for both nucleation and growth of amorphous aggregates within individual droplets, which subsequently transform to amyloid-like fibrils. Immobilized droplets neither show any nucleation nor any growth upon aging. Moreover, we found that exchange of materials with the dilute dispersed phase has negligible influence on the LSPT of HSA. Notably, binding of small ligands modulates the feasibility and kinetics of LSPT of HSA, suggesting a possible regulatory mechanism that cells utilize to control the dynamics of LSPT. Further, using a dynamic heterogeneous droplet assembly of two functional proteins, HSA and transferrin (Tf), we show an intriguing phenomenon within the fused droplets where both liquid-like and solid-like phases co-exist within the same droplet, which eventually transform to a mixed fibrillar assembly. These microscopic insights not only highlight the importance of inter-droplet interactions behind the LSPT of biomolecules but also showcase its adverse effect on the structure and function of other functional proteins in a crowded and heterogeneous protein assembly.