bluuebunny/crossref_metadata_2025 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

The 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

For 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

Progress 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

An 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
Probabilistic Brain MR Image Transformation Using Generative Models
Authors: Rezvani Sepideh , Moazami Saeed , Azevedo Christina J. , Oberai Assad A.
Published: November 2024

Brain MR image transformation, which is the process of transforming one type of MR image into another, is a critical neuroimaging task that is needed when the target image type is missing or corrupted. Accordingly, several methods have been developed to tackle this problem, with a recent focus on deep learning-based models. In this paper, we investigate the performance of the conditional version of three such probabilistic generative models, including conditional Generative Adversarial Networks (cGAN), Noise Conditioned Score Networks (NCSN), and De-noising Diffusion Probabilistic Models (DDPM). We also compare their performance against a more traditional deterministic U-Net based model. We train and test these models using MR images from publicly available datasets IXI and OASIS. For images from the IXI dataset, we conduct experiments on combinations of transformations between T1-weighted (T1), T2-weighted (T2), and proton density (PD) images, whereas for the OASIS dataset, we consider combinations of T1, T2, and Fluid Attenuated Inversion Recovery (FLAIR) images. In evaluating these models, we measure the similarity between the transformed image and the target image using metrics like PSNR and SSIM. In addition, for the three probabilistic generative models, we evaluate the utility of generating an ensemble of predictions by computing a metric that measures the variance in their predictions and demonstrate that it can be used to identify out-of-distribution (OOD) input images. We conclude that the NCSN model yields the most accurate transformations, while the DDPM model yields variance results that most clearly detect OOD inputs. We also note that while the results for the two diffusion models (NCSN and DDPM) are more accurate than those for the cGAN, the latter was significantly more efficient in generating multiple samples. Overall, our work demonstrates the utility of probabilistic conditional generative models for MR image transformation and highlights the role of generating an ensemble of outputs in identifying OOD input images.
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

Wild 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

In 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

Plant-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

The 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.

SIGNIFICANCE

KRAS 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

SARS-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

Formalin-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

Background

Adjunct 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.

Objective

To evaluate the optimal dose, safety and efficacy of adjunctive intra-arterial tenecteplase following successful EVT in LVO patients.

Methods and design

This 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.

Discussions

This 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 number

ChiCTR2300073787 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

Cells 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

Life 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

Abstract

Amino 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.
Enhancing information extraction from field potentials in electrophysiology studies
Authors: Akella Shailaja , Principe Jose C.
Published: November 2024

Multi-channel recordings from the brain serve as the primary approach to address various mechanistic and behavioral questions about neural activity and the underlying circuitry. While electrodes can detect neural activity at a distance from their source generators, activity from other concurrently active neural sources is also volume conducted to the electrodes, thus creating linear dependencies among the channels. These dependencies pose challenges in discerning specific communication patterns between different neuronal populations, and their behavioral implications. In this study, we demonstrate the capability of a marked point process (MPP) representation of channel activity, focusing on oscillatory bursts, to curtail the effects of volume conduction noise. By characterizing the localized spectral information within oscillatory bursts, we achieved a∼45% reduction in channel correlations across three recording modalities of field potentials (electroencephalography, electrocorticography, and local field potentials). We further provide evidence that the implemented sparse representation preserves both behavioral and causal information in the signal. We illustrate our findings with two examples: 1) retention of finger-level movement information in field potentials recorded from humans, demonstrated using a simple online classifier, and 2) retention of top-down connectivity information between the prefrontal and motor cortices of behaving rats. Overall, our results underscore the novelty of using a marked point process representation of oscillatory bursts to concisely encode behavioral and connectivity information while attenuating the effects of volume conduction from the causal signal sources.

Author summary

Multi-channel recordings from the brain are critical to understanding how different brain regions communicate and drive behavior. However, the electrodes that detect neural activity also pick up signals from other active neural sources, leading to misleading linear dependencies among the channels. In our study, we present a novel approach using a marked point process (MPP) representation of channel activity using oscillatory bursts, to minimize the interference caused by volume conduction noise. By isolating the spectral characteristics within these oscillatory bursts, we achieved a∼45% reduction in channel correlations across three different types of field potential recordings: electroencephalography (EEG), electrocorticography (ECoG), and local field potentials (LFPs). Importantly, our method not only reduces noise but also retains critical behavioral and causal information in the neural signals. Our findings reveal that a marked point process representation of oscillatory bursts provides a more precise and noise-resilient representation of neural activity. This advancement has significant implications for both understanding brain function and improving the accuracy of brain-machine interfaces.
sRNA-mediated crosstalk between cell wall stress and galactose metabolism inStaphylococcus aureus
Authors: Germain Maëliss , Robin Hugo , Huyen Kim Boi Le , Massier Sébastien , Nalpas Nicolas , Hardouin Julie , Bouloc Philippe , Rouillon Astrid , Chabelskaya Svetlana
Published: November 2024

Staphylococcus aureusis an opportunistic pathogen responsible for a wide range of diseases in humans. During infections, this bacterium is exposed to various stresses that target its cell wall, such as oxidative or acid environments as well as various cell wall-acting antimicrobials.S. aureushas effective regulatory systems for responding to environmental stresses enabling the expression of factors necessary for its survival. Bacterial small RNAs (sRNAs) play a crucial role in this adaptation process. In this study, we show that RsaOI, anS. aureussRNA, accumulates under acid stress conditions. This response is mediated via the two-component system VraSR, which is associated with the cell wall damage response. As a component of the VraSR regulon, RsaOI contributes to the survival ofS. aureusunder acid stress and affects its susceptibility to glycopeptide antibiotics. Our findings reveal that RsaOI targetslacABCDFEGoperon, which encodes components of tagatose pathway, a unique mechanism responsible for galactose metabolism inS. aureus. By antisense base pairing near the ribosome-binding site oflacD, RsaOI inhibits the expression of this gene, encoding tagatose-6-phosphate aldolase. This regulation disrupts the tagatose pathway, impairing galactose utilization inS. aureus. These findings highlight the role of RsaOI in the mediation between cell wall stress responses and specific metabolic pathway.
High-Throughput Screening Reveals Potential Inhibitors Targeting Trimethoprim-Resistant DfrA1 Protein inKlebsiella pneumoniaeandEscherichia coli
Authors: Hasnat Soharth , Rahman Soaibur , Alam Meherun Binta , Suin Farha Mohi , Yeasmin Farzana , Suha Tanjila , Supty Nahuna Tanjin , Sabila Sal , Chowdhury Animesh , Shahinuzzaman A.D.A. , Mahbub M Murshida , Hoque M. Nazmul , Islam Tofazzal
Published: November 2024

The DfrA1 protein provides trimethoprim resistance in bacteria, especiallyKlebsiella pneumoniaeandEscherichia coli, by modifying dihydrofolate reductase, which reduces the binding efficacy of the antibiotic. Thus, this study aimed to identify inhibitors of the trimethoprim-resistant DfrA1 protein through high-throughput computational screening of 3,601 newly synthesized chemical compounds sourced from theChemDivdatabase. We conducted high-throughput computational optimization and screening of a library containing 3,601 compounds against the DfrA1 protein fromK. pneumoniaeandE. colito identify potential drug candidates (DCs). Through this extensive approach, we identified six promising DCs, labeled DC1 to DC6, as potential inhibitors of DfrA1. Each DC demonstrated strong initial binding affinity and favorable chemical interactions with the DfrA1 binding sites when compared to the effective drug Iclaprim (effective antibiotic against DfrA1), used as a control. To validate these findings, we further investigated the molecular mechanisms of inhibition, focusing on the thermodynamic properties of the promising DCs. Furthermore, molecular dynamics simulation (MDS) validated the inhibitory efficacy of these six DCs against the DfrA1 protein. Our results showed that DC4 (an organoflourinated compound) and DC6 (a benzimidazol compound) showed superior efficacy against the DfrA1 protein than the control drug, particularly regarding stability, solvent-accessible surface area, solvent exposure, polarity, and binding site interactions, which influence their residence time and efficacy. Overall, findings of this study suggest that DC4 and DC6 have the potential to act as inhibitors against the DfrA1, offering promising prospects for the treatment and management of infections caused by trimethoprim-resistantK. pneumoniaeandE. coliin both humans and animals.
Pathogen threat proximity shapes host extracellular vesicle production in pre-infection response
Authors: Kołodziejska Klaudia , Szczepańska Agata , Pujol Nathalie , Pokrzywa Wojciech , Turek Michał
Published: November 2024

Extracellular vesicles (EVs) play a crucial role in immune responses, yet it remains unclear whether pathogen metabolites alone can stimulate EV production prior to infection. UsingCaenorhabditis elegans, we investigate this question through the lens of exophers – large, evolutionarily conserved EVs known to enhance proteostasis, extend lifespan, and improve reproductive fitness. Our study uncovers distinct regulatory mechanisms driving EV production in response to pathogen-derived volatile and non-volatile metabolites, providing insights into host-pathogen signaling before physical interaction. We reveal a sophisticated network that adjusts EV production based on pathogen proximity: non-volatile metabolites, signaling an immediate threat, activate immune-dependent EV pathways, while volatile metabolites, forewarning potential danger, initiate immunity-independent exopher production. Both responses rely on sensory neurons, with ASK, ADL, and AWC neurons playing central roles in each, and additional input from ASI, AWB, and ASH neurons specifically enhancing non-volatile-induced EV production. Multiple GPCRs, including SRI-19, SRI-36, SRI-39, and SRR-6, are crucial for the non-volatile response, with signal integration mediated by the AIB interneuron and NPR-9 receptor in both responses. Notably, volatile-induced exopher production enhances offspring survival against pathogens but compromises maternal survival. Thus, our study shows thatC. elegansdistinguishes pathogen proximity through metabolite types, activating EV-dependent physiological responses tailored to either immediate or anticipated threats to optimize survival across generations.
Stereocilium height changes can account for the calcium dependence of the outer-hair-cell bundle’s resting state
Authors: Chatterjee Rayan , Ó Maoiléidigh Dáibhid
Published: November 2024

Outer-hair-cell bundles are sensory organelles required for normal hearing in mammals. These bundles convert sound-induced forces into receptor currents. This conversion depends on the resting receptor current of each bundle, which increases when extracellular calcium is decreased to the physiological level. How extracellular calcium regulates the bundle’s resting state is not well understood. We propose a mechanism explaining how extracellular calcium can regulate the outer-hair-cell bundle’s resting state. Each bundle comprises filamentous stereocilia linked by gating springs that are attached to ion channels. Sound-induced forces deflect stereocilia, increasing and decreasing gating-spring tensions, opening and closing the ion channels, resulting in an oscillating receptor current. We hypothesize that decreasing extracellular calcium, decreases the heights of the shorter stereocilia, increasing resting gating-spring tensions, which increases the resting receptor current and decreases the bundle’s resting deflection. To determine the plausibility of this mechanism, we build a mathematical model of an outer-hair-cell bundle and calibrate the model using seven independent experimental observations. The calibrated model shows that the mechanism is quantitatively plausible and predicts that a decrease of only 10 nm in the heights of the shorter stereocilia when extracellular calcium is lowered is sufficient to explain the observed increase in the resting receptor current. The model predicts the values of nine parameters and makes several additional predictions.
Combining metabolic engineering and fermentation optimization to achieve cost-effective oil production byCutaneotrichosporon oleaginosus
Authors: Duman-Özdamar Zeynep Efsun , Veloo Rudolf Marcel , Tsepani Elena , Julsing Mattijs K. , Martins dos Santos Vitor A.P. , Hugenholtz Jeroen , Suarez-Diez Maria
Published: November 2024

Microbial oils, produced by oleaginous microorganisms, offer a sustainable alternative to plant-derived oils. Among these microorganisms,Cutaneotrichosporon oleaginosusattracts attention as a promising microbial cell factory for sustainable oil production due to its capacity to accumulate lipids with a similar composition to palm oil. AlthoughC. oleaginosuscan reach higher lipid contents than other oleaginous yeasts, suboptimal lipid yields and productivity limit economic feasibility. Enhanced productivity is necessary to have a feasible microbial oil production process viaC. oleaginosus. In this study, we followed a combinatorial approach for strain design and bioprocess development to improve the lipid content and lipid yield. Initially, we deployed a full factorial design with genetic factors (ATP-citrate lyase (ACL), acetyl-CoA carboxylase (ACC), threonine synthase (TS)) and carbon-to-nitrogen ratio (C/N) in the medium. The C/N ratio appeared to have the most impact on oil accumulation. Combined with genetic modifications, lipid content and lipid yield increased by 1.6-fold. In a two-stage fermentation approach at a 2L scale, the triple transformant overexpressingACL,ACC, andTSoutperformed the wild-type by achieving a lipid content of 75.4% (w/w) with lipid productivity of 0.40 g L-1h-1and around 0.30 g lipids/g glycerol. In all, we established a cultivation strategy and strain that reached almost the theoretical maximum yield, and highest lipid content reported for a medium containing glycerol as a carbon source. These results strengthen the basis of usingC. oleaginousas a platform for microbial oil production, thereby facilitating the development of processes substituting palm oil with a sustainable alternative.

Highlights

Full factorial design allowed selection of the best performer transformant.

Overexpression ofACL1, ACC,andTSinC. oleaginosusprovided higher lipid yield.

Two-stage fermentation enhanced the lipid content of wild-type and transformant.

Graphical Abstract
Disrupted activation of pain networks during attack imagery in patients with episodic migraine
Authors: Perdigão Alexandre , Esteves Inês , Fouto Ana R , Ruiz-Tagle Amparo , Caetano Gina , Gil-Gouveia Raquel , Figueiredo Patrícia
Published: November 2024

Migraine is a prevalent and disabling brain disorder characterized by recurrent headache attacks alternating with pain-free periods. Patients with migraine exhibit altered brain activation in response to experimental nociceptive stimuli compared to healthy controls, suggesting impaired pain inhibition mechanisms. However, the relevance of these alterations to the underlying pathophysiology of migraine attacks remains unclear. To explore this relationship, we propose a novel pain imagery paradigm aimed to induce brain activation patterns associated with migraine attacks specifically during pain-free.

The task required patients to alternate between imagining a severe migraine attack and being headache free, while healthy controls imagined severe physical head pain (e.g., toothache) and pain relief. We collected fMRI data from 14 episodic migraine without aura patients in the interictal phase and 15 healthy controls performing this task.

Both patients and controls activated pain-related brain areas during imagery of pain relative to pain relief. Moreover, controls also activated the medial pain system associated with pain inhibition and attentional modulation of pain via top-down pathways. In contrast, patients significantly deactivated these areas, namely the anterior cingulate cortex and dorsolateral prefrontal cortex. Collectively, our findings indicate altered functioning of pain networks in migraine patients, suggesting a disease-related dysregulation of pain inhibition. Eventually, the proposed attack imagery paradigm may provide a promising alternative to studies of pain mechanisms in migraine research.
Therapeutic window for treatment of prion disease defined by timed depletion of Prion protein
Authors: Bradford Barry M. , Marshall Alison , Tuzi Nadia L. , Brown Deborah , Kisielewski Dorothy , Summers Kim M. , Perry V. Hugh , Piccardo Pedro , Clarke Alan R. , Manson Jean C.
Published: November 2024

There is no effective treatment preventing the progression of neurodegenerative diseases such as prion and Alzheimer’s diseases. These fatal diseases of the central nervous system, involve progressive accumulation of a misfolded protein long before overt clinical signs of disease. Removal of prion protein early in the pathological process appears to halt the progression however, it is not known whether intervention at later disease stages could be effective. We investigated the potential for intervention throughout the course of prion disease, by developing a mouse model in whichPrnpexpression can be manipulated in a tissue specific and time dependent manner. DepletingPrnpfrom neuronal populations in CNS throughout the preclinical phase substantially prolonged incubation. The pathology was dramatically altered to a pattern of astrocytic associated prion deposition. However once overt clinical symptoms of disease were apparentPrnpdepletion did not alter disease progression. This study establishes a wide window for intervention, and suggests timely treatment could delay the onset of clinical disease potentially well beyond the lifetime of an individual.
High Resolution Spatial Mapping of Microbiome-Host Interactions viain situPolyadenylation and Spatial RNA Sequencing
Authors: Ntekas Ioannis , Takayasu Lena , McKellar David W. , Grodner Benjamin M. , Holdener Chase , Schweitzer Peter , Sauthoff Maya , Shi Qiaojuan , Brito Ilana L. , De Vlaminck Iwijn
Published: November 2024

Inter-microbial and host–microbial interactions are thought to be critical for the functioning of the gut microbiome, but few tools are available to measure these interactions. Here, we report a method for unbiased spatial sampling of microbiome-host interactions in the gut at one micron resolution. This method combines enzymaticin situpolyadenylation of both bacterial and host RNA with spatial RNA-sequencing. Application of this method in a mouse model of intestinal neoplasia revealed the biogeography of the mouse gut microbiome as function of location in the intestine, frequent strong inter-microbial interactions at short length scales, shaping of local microbiome niches by the host, and tumor-associated changes in the architecture of the host-microbiome interface. This method is compatible with broadly available commercial platforms for spatial RNA-sequencing, and can therefore be readily adopted to broadly study the role of short-range, bidirectional host-microbe interactions in microbiome health and disease.
Dendritic synaptic integration modes underin vivo-like states
Authors: Ceballos Cesar C. , Pena Rodrigo F. O.
Published: November 2024

The neural code remains undiscovered and understanding synaptic input integration underin vivo-like conditions is just the initial step toward unraveling it. Synaptic signals generate fast dendritic spikes through two main modes of temporal summation: coincidence detection and integration. In coincidence detection, dendrites fire only when multiple incoming signals arrive in rapid succession, whereas integration involves summation of postsynaptic potentials over longer periods with minimal membrane leakage. This process is influenced by ionic properties, especially as the membrane potential approaches the firing threshold, where inactivating currents play a critical role. However, the modulation of temporal summation by these currents underin vivo-like conditions has not been thoroughly studied. In our research, we used computer simulations to investigate how three inactivating currents — A-type potassium, T-type calcium, and transient sodium — affect temporal summation. We found that calcium and sodium currents promote integrative behavior in dendrites, while potassium currents enhance their ability to act as coincidence detectors. By adjusting the levels of these currents in dendrites, neurons can flexibly switch between integration and coincidence detection modes, providing them with a versatile mechanism for complex tasks like multiplexing. This flexibility could be key to understanding how neural circuits process information in real time.
Connectome-based models of feature selectivity in a cortical circuit
Authors: Buendía Victor , Biggioggera Jacopo , Sanzeni Alessandro
Published: November 2024

Feature selectivity, the ability of neurons to respond preferentially to specific stimulus configurations, is a fundamental building block of cortical functions. Various mechanisms have been proposed to explain its origins, differing primarily in their assumptions about the connectivity between neurons. Some models attribute selectivity to structured, tuning-dependent feedforward or recurrent connections, whereas others suggest it can emerge within randomly connected networks when interactions are sufficiently strong. This range of plausible explanations makes it challenging to identify the core mechanisms of feature selectivity in the cortex. We developed a novel, data-driven approach to construct mechanistic models by utilizing connectomic data—synaptic wiring diagrams obtained through electron microscopy—to minimize preconceived assumptions about the underlying connectivity. With this approach, leveraging the MICrONS dataset [1], we investigate the mechanisms governing selectivity to oriented visual stimuli in layer 2/3 of mouse primary visual cortex. We show that connectome-constrained network models replicate experimental neural responses and point to connectivity heterogeneity as the dominant factor shaping selectivity, with structured recurrent and feedforward connections having a noticeable but secondary effect in its amplification. These findings provide novel insights on the mechanisms underlying feature selectivity in cortex and highlight the potential of connectome-based models for exploring the mechanistic basis of cortical functions.