bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Categorical Data with Logistic Regression and the Cochran–Mantel–Haenszel Tests in Biological Experiments
Authors: Androwski Rebecca , Popovitchenko Tatiana , Smart Joelle , Ogino Sho , Wang Guoqiang , Rongo Christopher , Driscoll Monica , Roy Jason 
Published: November 2024

AbstractThe choice of statistical test is a fundamentally important one when analyzing experimental data. Here, we consider the question of categorical data, defined by their properties (for example color) rather than by continuous numbering. Using simple and complex example datasets generated fromCaenorhabditis elegansresearch, we conduct a statistical analysis of (1) a rare cellular event involving the formation of a neuronal extrusion called an exopher, and of (2) a variable behavioral response across a timescale. Two tests we use here are the Cochran– Mantel–Haenszel (CMH) test and logistic regression. These two tests pose practical challenges to researchers that include lack of easy access to statistical software and the need for prior programming knowledge. To this end we provide step-by-step tutorials and example code. We emphasize the flexibility of logistic regression in handling both simple and complex datasets, emphasizing the capacity of logistic regression to provide more comprehensive insights into experimental outcomes than simpler tests like CMH. By analyzing real biological examples and demonstrating their analysis with R code, we provide a practical guide for biologists to enhance the rigor and reproducibility of categorical data analysis in experimental studies.
AlphaFold predictions on whole genomes at a glance
Authors: Cazals Frédéric , Sarti Edoardo 
Published: November 2024

AbstractFor model organisms,AlphaFoldpredictions show that 30% to 40% of amino acids have a (very) low pLDDT confidence score. This observation, combined with the method’s high complexity, commands to investigate difficult cases, the link with IDPs/IDRs, and potential hallucinations. We do so via four contributions. First, we provide a multiscale characterization of stretches with coherent pLDDT values along the sequence, an important analysis for model quality assessment. Second, we leverage the 3D atomic packing properties of predictions to represent a structure as a distribution. This distribution is then mapped into the so-called2D arity map, which simultaneously performs dimensionality reduction and clustering, effectively summarizing all structural elements across all predictions. Third, using the database of domainsECOD, we study potential biases inAlphaFoldpredictions at the sequence and structural levels, identifying a specific region of the arity map populated with low quality 3D domains. Finally, with a focus on proteins with intrinsically disordered regions (IDRs), using DisProt and AIUPred, we identify specific regions of the arity map characterized by false positive and false negatives in terms of IDRs.Summarizing, the arity map sheds light on the accuracy ofAlphaFoldpredictions, both in terms of 3D domains and IDRs.
in vivo-like Scaffold-free 3Din vitroModels of Muscular Dystrophies: The Case for Anchored Cell Sheet Engineering in Personalized Medicine
Authors: Shahin-Shamsabadi Alireza , Cappuccitti John 
Published: November 2024

AbstractProgress in understanding the underlying mechanisms of muscle dystrophies and finding effective treatments for them has been hindered by the absence of relevantin vitromodels for biomedical research. In this study, an entirely scaffold-free cell sheet engineering-based platform is used to make suchin vitromodels using patient-specific cells. Unlike reductionist bottom-up approaches, this holistic biofabrication method, termed anchored cell sheet engineering, effectively replicated mature cell phenotypes and tissue- and disease-specific ECM deposited by the cells themselves. Robust anchored 3D muscle fibers were developed using primary cells from both healthy individuals and patients with Duchenne dystrophy and Myotonic dystrophy type 1. Through a combination of histology, immunostaining, and proteomics analysis, it was demonstrated that these models formed mature constructs that closely resembledin vivoconditions, outperforming traditional 2D cultures in their translation potential. Models of diseased tissues, analyzed through various analysis, accurately reflected key phenotypic features of the respective diseases. Furthermore, when treated with therapeutically beneficial drugs, the detailed changes in their proteomic profiles were documented. This novelin vitromodeling approach, compared to other 3D techniques that use exogenous scaffolding or bioink, provides a promising platform for advancing the development of muscle dystrophy models, among other conditions.
Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance
Authors: Qiu Sizhe , Wang Nan-Kai , Lu Yishun , Gong Jin-Song , Shi Jin-Song , Yang Aidong 
Published: November 2024

AbstractAn accurate deep learning predictor of enzyme optimal pH is essential to quantitatively describe how pH influences the enzyme catalytic activity. CatOpt, developed in this study, outperformed existing predictors of enzyme optimal pH (RMSE=0.833 and R2=0.479), and could provide good interpretability with informative residue attention weights. The classification of acidic and alkaline enzymes and prediction of enzyme optimal pH shifts caused by point mutations showcased the capability of CatOpt as an effective computational tool for identifying enzyme pH preferences. Furthermore, a single point mutation designed with the guidance of CatOpt successfully enhanced the activity ofPyrococcus horikoshiidiacetylchitobiose deacetylase at low pH (pH=4.5/5.5) by approximately 7%, suggesting that CatOpt is a promisingin-silicoenzyme design tool for pH-dependent enzyme activities.Graphical abstract
Unraveling Honey Bee’s Waggle Dances in Natural Conditions with Video-Based Deep Learning
Authors: Grison Sylvain , Siddaganga Rajath , Hegde Shrihari , Burridge James , Blok Pieter M. , Krishnan Smitha , Brockmann Axel , Guo Wei 
Published: November 2024

AbstractWild and managed honey bees, crucial pollinators for both agriculture and natural ecosystems, face challenges due to industrial agriculture and urbanization. Understanding how bee colonies utilize the landscape for foraging is essential for managing human-bee conflicts and protecting these pollinators to sustain their vital pollination services. To understand how the bees utilize their surroundings, researchers often decode bee waggle dances, which honey bee workers use to communicate navigational information of desirable food and nesting sites to their nest mates. This process is carried out manually, which is time-consuming, prone to human error and requires specialized skills.We address this problem by introducing a novel deep learning-based pipeline that automatically detects and measures waggle runs, the core movement of the waggle dance, under natural recording conditions for the first time. We combined the capabilities of the action detector YOWOv2 and the DeepSORT tracking method, with the Principal Component Analysis to extract dancing bee bounding boxes and the angles and durations within waggle runs.The presented pipeline works fully automatically with videos taken from wildApis dorsatacolonies in its natural environment, and can be used for any honey bee species. Comparison of our pipeline with analyses made by human experts revealed that our procedure was able to detect 93% of waggle runs on the testing dataset, with a run duration Root Mean Squared Error (RMSE) of less than a second, and a run angle RMSE of 0.14 radians. We also assessed the generalizability of our pipeline to previously unseen recording conditions, successfully detecting 50% of waggle runs performed byApis melliferabees from a colony managed in Tokyo, Japan. In parallel, we discovered the most appropriate values of the model’s hyperparameters for this task.Our study demonstrates that a deep learning-based pipeline can successfully and automatically analyze the waggle runs ofApis dorsatain natural conditions and generalize to other bee species. This approach enables precise measurement of direction and duration, enabling the study of bee foraging behavior on an unprecedented scale compared to traditional manual methods contributing to preserving biodiversity and ecosystem services.
New technologies for hair follicle growth and regeneration: comparison between different commercial products contain exosomes
Authors: Ferruggia Greta , Zimbone Massimo , Brundo Maria Violetta 
Published: November 2024

AbstractIn people suffering from baldness, stem cells remain inactive and are unable to regenerate new hair. For this reason, research has focused on factors (including growth factors and cytokines) or new technologies that could favor the transition from telogen to anagen and the subsequent stimulation of hair growth and regeneration. In our study we comparedin vitro, on cultures of human hair follicles, the effect on hair growth and on the regeneration of the dermal papilla of two products containing exosomes of different origins obtained with innovative technologies. In particular, we used a product in which the presence of exosomes and proteins obtained from stem cells derived from the umbilical cord lining is declared and a product that contains exosomes obtained from bovine colostrum and passively loaded with a mixture of growth factors and cytokines purified from bovine colostrum. The analyzes demonstrated a notable increase in the growth and regeneration of the bulbs the dermal papilla in the samples treated with the product containing exosomes obtained from bovine colostrum compared to product containing exosomes from mesenchymal stem cells which was comparable to the result obtained in the control samples (untreated). Therefore, the use of products containing exosomes, growth factors and cytokines purified from colostrum could be successfully used as possible new treatments capable of significantly slow hair loss and promote the growth of new hair.
Plasmids, Prophages and Defense Systems are Depleted from Plant Microbiota Genomes
Authors: Bograd Avi , Oppenheimer-Shaanan Yaara , Levy Asaf 
Published: November 2024

AbstractPlant-associated bacteria significantly impact plant growth and health. Understanding how bacterial genomes adapt to plants can provide insights into their growth promotion and virulence functions. Here, we compared 38,912 bacterial genomes and 6,073 metagenomes to explore the distribution of mobile genetic elements and defense systems in plant-associated bacteria. We reveal a consistent taxon-independent depletion of prophages, plasmids, and defense systems in plant-associated bacteria, particularly in the phyllosphere, compared to other ecosystems. The mobilome depletion suggests the presence of unique ecological constraints or molecular mechanisms exerted by plants to control the bacterial mobilomes independently of the bacterial defense.
Combinations of RAS pathway inhibitors with targeted agents are active in spheroids of patient-derived cells with oncogenic KRAS variants from multiple cancer types
Authors: Davoudi Zahra , Dexheimer Thomas S. , Coussens Nathan P. , Silvers Thomas , Morris Joel , Takebe Naoko , Doroshow James H. , Teicher Beverly A. 
Published: November 2024

ABSTRACTThe KRAS gene is among the most frequently altered genes in cancer and the KRAS protein was long deemed undruggable. Recent strategies to target oncogenic KRAS have included both direct inhibition of the KRAS protein and indirect inhibition of its activity by targeting upstream and downstream signaling pathway mediators. A high-throughput screen of multi-cell type tumor spheroids was designed to identify active combinations of targeted small molecules and KRAS pathway inhibitors. Inhibitors of the non-receptor protein tyrosine phosphatase SHP2 and the guanine nucleotide exchange factor SOS1 were tested to evaluate indirect upstream pathway inhibition, while sotorasib directly inhibited the KRAS G12C variant. As single agents, sotorasib and the SHP2 inhibitor batoprotafib (TNO155) exhibited selectivity towards spheroids with KRAS G12C, whereas the SOS1 inhibitor BI-3406 showed varying activity across KRAS variants. Vertical inhibition of the RAS/MEK/ERK pathway by targeting SHP2 or SOS1 and the downstream kinases MEK (trametinib) or ERK (temuterkib) was highly effective. Inhibition of upstream tyrosine receptor kinases with nintedanib in combination with batoprotafib or BI-3406 was also effective, and in combination with sotorasib, demonstrated synergy in spheroids harboring KRAS G12C. Dual inhibition of the RAS/MEK/ERK and PI3K/AKT/mTOR pathways with batoprotafib or sotorasib with either the mTORC1/2 inhibitor sapanisertib or the AKT inhibitor ipatasertib demonstrated combination activity, primarily in spheroids harboring KRAS G12C. Combination of the BCL-2 inhibitor venetoclax with sotorasib, batoprotafib or BI-3406 resulted in additive and synergistic cytotoxicity. Lastly, concurrent inhibition of the KRAS pathway with sotorasib and batoprotafib demonstrated combination activity in spheroids containing KRAS G12C.SIGNIFICANCEKRAS variants are oncogenic drivers for a range of human cancers. Multiple combinations of small molecule agents that target RAS signaling were screened and reduced the viability of multi-cell type spheroid models for a variety of human solid tumors. Combinations warranting further testing were identified.
Antibody evasiveness of SARS-CoV-2 subvariants KP.3.1.1 and XEC
Authors: Wang Qian , Guo Yicheng , Mellis Ian A. , Wu Madeline , Mohri Hiroshi , Gherasim Carmen , Valdez Riccardo , Purpura Lawrence J. , Yin Michael T. , Gordon Aubree , Ho David D. 
Published: November 2024

AbstractSARS-CoV-2 continues to evolve and spread around the world, and it remains critical to understand the functional consequences of mutations that lead to dominant viral variants. KP.3.1.1 is currently the most prevalent subvariant worldwide, while the recombinant subvariant XEC is exhibiting the fastest growth rate. Here we measured the in vitro neutralization of KP.3.1.1 and XEC by human sera, monoclonal antibodies, and soluble hACE2 receptor relative to their parental subvariants KP.3 and JN.1. KP.3.1.1 and XEC were slightly more resistant (1.3-1.6-fold) than KP.3 to serum neutralization, and the resultant antigenic map showed that the new subvariants are antigenically similar. Both also demonstrated greater resistance to neutralization by select monoclonal antibodies and soluble hACE2, all of which target the top of the viral spike. Our findings suggested that upward motion of the receptor-binding domain in spike is partially hindered by the N-terminal-domain mutations found KP.3.1.1 and XEC, thereby allowing these subvariants to better evade serum antibodies that target the viral spike when it is in the up position and thus having a growth advantage in the population.
High-throughput proteomic and phosphoproteomic analysis of formalin-fixed paraffin-embedded tissue
Authors: Haines Moe , Thorup John R. , Gohsman Simone , Ctortecka Claudia , Newton Chelsea , Rohrer Dan C. , Hostetter Galen , Mani D. R. , Gillette Michael A. , Satpathy Shankha , Carr Steven A. 
Published: November 2024

AbstractFormalin-fixed, paraffin-embedded (FFPE) patient tissues are a valuable resource for proteomic studies with the potential to associate the derived molecular insights with clinical annotations and outcomes. Here we present an optimized, partially automated workflow for FFPE proteomics combining pathology-guided macro-dissection, Adaptive Focused Acoustics (AFA) sonication for lysis and decrosslinking, S-Trap digestion to peptides, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using Orbitrap, Astral or timsTOF HT instrumentation. The workflow enables analysis of up to 96 dissected FFPE tissue samples or 10 µm scrolls, identifying 8,000-10,000 unique proteins per sample with median CVs <20%. Key optimizations include improved tissue lysis strategies, protein quantification for normalization, and peptide cleanup prior to LC-MS/MS analysis. Application to lung adenocarcinoma (LUAD) FFPE blocks confirms the platform’s effectiveness in processing complex, clinically relevant samples, achieving deep proteome coverage and quantitative robustness comparable to TMT-based methods. Using the newly released Orbitrap Astral with short, 24-minute gradients, the workflow identifies up to ∼10,000 unique proteins and ∼11,000 localized phosphosites in LUAD FFPE tissue. This high-throughput, scalable workflow advances biomarker discovery and proteomic research in archival tissue samples.
Safety and Efficacy of Adjunctive Intra-arterial Tenecteplase following Successful Thrombectomy in Patients with Large Vessel Occlusion: Study Rationale and Design of A Phase 1/2 Randomized Clinical Trial
Authors: Hou Xianhua , Huang Jiacheng , Guo Changwei , Wang Li , He Yuxuan , Chen Lin , Liu Qu , Wu Junhua , Wu Min , Wu Yazhou , Nguyen Thanh N. , Nogueira Raul G. , Wang Duolao , Saver Jeffrey L. , Zi Wenjie , Zhou Zhenhua 
Published: November 2024

AbstractBackgroundAdjunct intra-arterial thrombolysis has been shown to potentially improve clinical outcomes in patients with large vessel occlusion (LVO) stroke who have undergone successful endovascular thrombectomy (EVT). Tenecteplase, known for its enhanced fibrin specificity and extended activity duration, may be a better choice than alteplase for intra-arterial thrombolysis. However, the optimal dose, safety and efficacy of intra-arterial tenecteplase remain unclear.ObjectiveTo evaluate the optimal dose, safety and efficacy of adjunctive intra-arterial tenecteplase following successful EVT in LVO patients.Methods and designThis study is a two-part, phase Ib/IIa, multicenter, open-label, 14+8 dose-escalation (Part I) and dose-expansion (Part II) trial conducted in China involving patients LVO patients who achieved successful EVT (defined as the expanded Thrombolysis in Cerebral Infarction [eTICI] grade of 2b50 or higher) within 24 hours of last known well. In Part Ib, the dose escalation will be conducted in up to four tiers of 14+8 patients, starting at 0.03125 mg/kg, to a planned maximum of 0.1875mg/kg, and the primary outcome is symptomatic intracranial hemorrhage within 24 hours. In Part IIa, 157 patients will be randomized among two selected safe doses and a placebo, and the primary outcome will be the proportion of patients with nondisabled outcome (modified Rankin Scale score of 0 to 1) at 90 days.DiscussionsThis pivotal trial will provide important data on adjunctive intra-arterial tenecteplase following successful EVT in patients with acute ischemic stroke due to LVO and may support advance of treatment standards.Trial registry numberChiCTR2300073787 and ChiCTR2400080624 (www.chictr.org.cn).
Substrate Stiffness Dictates Unique Doxorubicin-induced Senescence-associated Secretory Phenotypes and Transcriptomic Signatures in Human Pulmonary Fibroblasts
Authors: Du Huixun , Rose Jacob P. , Bons Joanna , Guo Li , Valentino Taylor R. , Wu Fei , Burton Jordan B. , Basisty Nathan , Manwaring-Mueller Max , Makhijani Priya , Chen Nan , Chang Veronica , Winer Shawn , Campisi Judith , Furman David , Nagy Andras , Schilling Birgit , Winer Daniel A. 
Published: November 2024

AbstractCells are subjected to dynamic mechanical environments which impart forces and induce cellular responses. In age-related conditions like pulmonary fibrosis, there is both an increase in tissue stiffness and an accumulation of senescent cells. While senescent cells produce a senescence-associated secretory phenotype (SASP), the impact of physical stimuli on both cellular senescence and the SASP is not well understood. Here, we show that mechanical tension, modeled using cell culture substrate rigidity, influences senescent cell markers like SA-β-gal and secretory phenotypes. Comparing human primary pulmonary fibroblasts (IMR-90) cultured on physiological (2 kPa), fibrotic (50 kPa), and plastic (approximately 3 GPa) substrates, followed by senescence induction using doxorubicin, we identified unique high-stiffness-driven secretory protein profiles using mass spectrometry and transcriptomic signatures, both showing an enrichment in collagen proteins. Consistently, clusters of p21+ cells are seen in fibrotic regions of bleomycin induced pulmonary fibrosis in mice. Computational meta-analysis of single-cell RNA sequencing datasets from human interstitial lung disease confirmed these stiffness SASP genes are highly expressed in disease fibroblasts and strongly correlate with mechanotransduction and senescence-related pathways. Thus, mechanical forces shape cell senescence and their secretory phenotypes.
Nitrogenase structural evolution across Earth’s history
Authors: Cuevas-Zuviría Bruno , Detemple Franka , Amritkar Kaustubh , Garcia Amanda K. , Seefeldt Lance C. , Einsle Oliver , Kaçar Betül 
Published: November 2024

ABSTRACTLife on Earth is more than 3.5 billion years old—nearly as old as the age of the planet. Over this vast expanse of time, life and its biomolecules adapted to and triggered profound changes to the Earth’s environment. Certain critical enzymes evolved early in the history of life and have persisted through planetary extremes. While sequence data is widely used to trace evolutionary trajectories, enzyme structure remains an underexplored resource for understanding how proteins evolve over long timescales. Here, we implement an integrated approach to study nitrogenase, an ancient, globally critical enzyme essential for nitrogen fixation. Despite the ecological diversity of its host microbes, nitrogenase has strict functional limitations, including extreme oxygen sensitivity, energy requirements and substrate availability. By combining phylogenetics, ancestral sequence reconstruction, protein crystallography and deep-learning based structural prediction, we resurrected three billion years of nitrogenase structural history. We present the first effort to predict all extant and ancestral structures along the evolutionary tree of an enzyme and present a total of ∼5000 structures. Our approach lays the foundation for reconstructing key structural constraints that influence protein evolution and studying ancient enzyme evolution in the light of phylogenetic and environmental change.
Profiling amino acid substitution on proteomic scale unveils novel protein regulation in cancer diseases
Authors: Zhao Peize , Dai Shuaijian , Lai Shengzhi , Zhou Chen , Li Ning , Yu Weichuan 
Published: November 2024

AbstractAmino acid substitutions (AA subs) play an important role in regulating cellular activities, encompassing complex cell signaling and cycling in both normal and cancer cells. Profiling AA subs at a proteomic level has been a challenging issue in the past. In this study, we analyze a large collection of proteomic data originating from 537 patients across five cancer patient cohorts using a novel mass spectrometry data analysis tool, named PIPI3. The pervasive patterns of AA subs are present in essential proteins like exosome-related proteins, actin filament binding proteins, and proteins in glycolysis and gluconeogenesis pathways of cancer cells. The novel identified AA subs are assumed to be caused during the protein synthesis process, which cannot be identified by traditional genome or transcriptome-based methods. Thus, this proteomic scale analysis provides insights into novel regulation mechanisms of AA subs in carcinogenesis, the outcome of which may be applied in cancer diagnosis and interventions.