bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamics and Activation of Membrane-Bound B Cell Receptor Assembly
Authors: Do Hung N. , Zhao Mingfei , Munir Alam S. , Gnanakaran S. 
Published: July 2024

AbstractB-cell receptor complexes (BCR) are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models. Recently, the first cryo-EM structures of the human B-cell antigen receptor of the IgM and IgG isotypes were published that validated the asymmetric organization of the BCR complex. Here, we have carried out extensive molecular dynamics simulations to probe the conformational changes upon antigen binding and the influence of the membrane lipids. We identified two critical dynamical events that could be associated with antigen-dependent activation of BCR. First, antigen binding caused increased flexibility in regions distal to the antigen binding site. Second, antigen binding altered the rearrangement of IgM transmembrane helices, including the relative interaction of Igα/Igβ that mediates intracellular signaling. Furthermore, these transmembrane rearrangements led to changes in localized lipid composition.
From resting state to inactivation: the dynamic journey of the inactivation peptide in Shaker Kv channels
Authors: Pandey Roshan , Kalstrup Tanja , Blunck Rikard 
Published: July 2024

AbstractShaker Kv channels inactivate rapidly to culminate the action potential and maintain the homeostasis of excitable cells. The so-called N-type inactivation is caused by the first 46 amino acids of the N-terminus of the channel, known as the inactivation peptide. Numerous mutational studies have characterized N-type inactivation functionally, however, the position of the inactivation peptide in the resting state and its transition during inactivation is still debated. Here, we tracked the movement of the inactivation peptide during inactivation using voltage clamp fluorometry. By inserting an unnatural amino acid, 3-[(6-acetyl-2-naphthalenyl) amino]-L-alanine (Anap), which is sensitive to changes in environment, we identified the movements of ball and chain separately. Our data suggests that N-type inactivation occurs in a biphasic movement by first releasing the IP, which then blocks the pore from the cytoplasmic side. To further narrow down the resting position of the inactivation peptide, we used Lanthanide-based Resonance Energy transfer and transition metal (tm)FRET. We propose that the inactivation peptide is located in the window formed by the channel and the T1 domain, interacting with the acidic residues of the T1 domain.
Enhancing Retrieval Capacity of the Predictive Brain through Dorsolateral Prefrontal Cortex Intervention
Authors: Szücs-Bencze Laura , Vékony Teodóra , Pesthy Orsolya , Kocsis Krisztián , Kincses Zsigmond Tamás , Szabó Nikoletta , Nemeth Dezso 
Published: July 2024

AbstractThe ability to extract spatial or temporal regularities across experiences is crucial for skill development and predictive processes. The prefrontal cortex (PFC) plays a key role in modulating competitive memory systems, supporting declarative/episodic memory as opposed to statistical learning. This regulatory role may explain findings of improved acquisition and consolidation of statistical regularities following the suppression of dorsolateral PFC (DLPFC) by repetitive transcranial magnetic stimulation (rTMS). This raises a key question: Is access to models and prior statistical knowledge also modulated by the DLPFC? This preregistered study provides new insights by examining the role of the DLPFC in retrieving pre-existing knowledge of temporally distributed statistical regularities. Using a probabilistic learning task, healthy participants engaged in implicit statistical learning for 25 minutes. After a 24-hour consolidation period, participants received either 1 Hz rTMS or sham stimulation over the left, right, or bilateral DLPFC for 10 minutes before retesting. We found more effective access to statistical regularities in the bilateral DLPFC group compared to the sham group. Our results suggest that DLPFC suppression enhances the retrieval of statistical knowledge, particularly when interhemispheric compensatory mechanisms are prevented. These findings contribute to understanding competitive memory systems and offer implications for cognitive enhancement strategies.
On the validity of electric brain signal predictions based on population firing rates
Authors: Ness Torbjørn V. , Tetzlaff Tom , Einevoll Gaute T. , Dahmen David 
Published: July 2024

AbstractNeural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately predict these brain signals. Simulations of neural activity at the population level often rely on point-neuron network models or firing-rate models. While these simplified representations of neural activity are computationally efficient, they lack the explicit spatial information needed for calculating LFP/EEG signals. Different heuristic approaches have been suggested for overcoming this limitation, but the accuracy of these approaches has not fully been assessed. One such heuristic approach, the so-called kernel method, has previously been applied with promising results and has the additional advantage of being well-grounded in the bio-physics underlying electric brain signal generation. It is based on calculating rate-to-LFP/EEG kernels for each synaptic pathway in a network model, after which LFP/EEG signals can be obtained directly from population firing rates. This amounts to a massive reduction in the computational effort of calculating brain signals because the brain signals are calculated for each population instead of for each neuron. Here, we investigate how and when the kernel method can be expected to work, and present a theoretical framework for predicting its accuracy. We show that the relative error of the brain signal predictions is a function of the single-cell kernel heterogeneity and the spike-train correlations. Finally, we demonstrate that the kernel method is most accurate for the dominating brain signal contributions. We thereby further establish the kernel method as a promising approach for calculating electric brain signals from large-scale neural simulations.
How cells align to structured collagen fibrils: A hybrid cellular Potts and molecular dynamics model with dynamic mechanosensitive focal adhesions
Authors: Keijzer Koen A.E. , Tsingos Erika , Merks Roeland M.H. 
Published: July 2024

AbstractMany mammalian cells, including endothelial cells and fibroblasts, tend to align and elongate with the orientation of extracellular matrix (ECM) fibers in a gel when cultured in vitro. During cell elongation, clusters of focal adhesions (FAs) form near the poles of the elongating cells. FAs are mechanosensitive clusters of adhesions that grow under mechanical tension due to the cells’ pulling on the ECM, and shrink when the tension is released. Using a mathematical modeling approach, we study the hypothesis that reciprocity between cells and the ECM drives cell shape changes. We show that FAs are preferentially stabilized along the orientation of ECM fibers, where the cells can generate more tension than perpendicular to the ECM fibers. We present a hybrid cellular Potts model that represents the ECM as an off-the-lattice network of cross-linked deformable fibers, whereas the cell is represented on the lattice. Multiple FAs are modeled individually by an independent rate of FA assembly and a mechanoresponsive FA disassembly. The resulting computational model predicts stiffness-dependent cell spreading and local ECM remodelling, and ECM-alignment dependent cell elongation. The effects combined suffice to explain how cell morphology is determined by local ECM structure and mechanics.
Exercise training at different intensities induces heat stress, disrupts barrier function and alters microbiota in the gut of mice
Authors: Lian Puqiao , Kovynev Artemiy , Wang Lei , Pronk Amanda C. M. , Verhoeven Aswin , Giera Martin , Thijssen Suzan , Martínez Téllez Borja , Kooijman Sander , Rensen Patrick C. N. , Timmerman Harro , Wichers Harry J. , Henricks Paul A. J. , Folkerts Gert , Schönke Milena , Braber Saskia 
Published: July 2024

AbstractExercise is generally beneficial for health but strenuous exercise can have detrimental effects on the gastrointestinal tract. The combination of ischemia and heat shock during exercise is a crucial contributor to intestinal epithelial damage. Growing evidence points towards an important regulatory role of gut microbes in intestinal homeostasis. Here, we characterize and compare the effects of moderate and vigorous exercise training on intestinal epithelial damage, stress response, inflammatory response, and gut microbiota alterations in mice and investigate the mechanisms underlying exercise-induced intestinal injury. Exercise training for six weeks caused heat stress in the intestine, resulting in the disruption of the intestinal epithelial barrier and local inflammation. This was characterized by increased colonic HSP-70 and HSF-1 protein expression, increased epithelial permeability, decreased colonic expression of tight junction proteins ZO-1 and occludin and intestinal morphological changes. Daily moderate exercise training caused hereby more severe injury than vigorous training on alternating days. Furthermore, exercise training altered the gut microbiota profile. The abundance ofLactobacillaceaewas reduced, potentially contributing to the deteriorated intestinal status, while the abundance of short-chain fatty acid-producingLachnospiraceaewas increased, especially following vigorous training. This increase in short-chain fatty acid-producing bacteria following vigorous training possibly counteracted the impairment of the intestinal barrier function. In summary, exercise disrupts the intestinal barrier function, with vigorous exercise training with intermittent rest days being less damaging than daily moderate exercise training.
Animacy semantic network supports implicit causal inferences about illness
Authors: Hauptman Miriam , Bedny Marina 
Published: July 2024

AbstractInferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that implicit causal inferences about biological processes (e.g., illness) depend on the animacy semantic network. Participants (n=20) undergoing fMRI read two-sentence vignettes that elicited causal inferences across sentences, either about the emergence of illness or about the mechanical breakdown of inanimate objects, in addition to noncausal control vignettes. All vignettes were about people and were linguistically matched. The same participants performed localizer tasks: language, logical reasoning, and mentalizing. Inferring illness causes, relative to all control conditions, selectively engaged a portion of the precuneus (PC) previously implicated in the semantic representation of animates (e.g., people, animals). Neural responses to causal inferences about illness were adjacent to but distinct from responses to mental state inferences, suggesting a neural mind/body distinction. We failed to find evidence for domain-general responses to causal inference. Implicit causal inferences are supported by content-specific semantic networks that encode causal knowledge.
Lipid Organization by the Caveolin-1 Complex
Authors: Liebl Korbinian , Voth Gregory A. 
Published: July 2024

AbstractCaveolins are lipid-binding proteins that can organize membrane remodeling and oligomerize into the 8S-complex. The CAV1 8S-complex comprises a disk-like structure, about 15nm in diameter, with a central beta barrel. Further oligomerization of 8S-complexes remodels the membrane into caveolae vessels, with a dependence on cholesterol concentration. However, the molecular mechanisms behind membrane remodeling and cholesterol filtering are still not understood. Performing atomistic Molecular Dynamics simulations in combination with advanced sampling techniques, we describe how the CAV1-8S complex bends the membrane and accumulates cholesterol. Here, our simulations show an enhancing effect by the palmitoylations of CAV1, and we predict that the CAV1-8S complex can extract cholesterol molecules from the lipid bilayer and accommodate them in its beta barrel. Through backmapping to the all-atom level we also conclude that the Martini v2 coarse-grained forcefield overestimates membrane bending, as the atomistic simulations exhibit only very localized bending.
Enhancing the accuracy of genome-scale metabolic models with kinetic information
Authors: Lázaro Jorge , Wongprommoon Arin , Júlvez Jorge , Oliver Stephen G. 
Published: July 2024

ABSTRACTMetabolic models can be used to analyze and predict cellular features such as growth, gene essentiality, and product formation. There are several metabolic models but two of the main types are the constraint-based models and the kinetic models. Constraint-based models usually account for a large subset of the metabolic reactions of the organism and, in addition to the reaction stoichiometry, these models can accommodate gene regulation and constant flux bounds of the reactions. Constraint-based models are mostly limited to the steady state and it is challenging to optimize competing objective functions. On the other hand, kinetic models contain detailed kinetic information of a relatively small subset of metabolic reactions; thus, they can only provide precise predictions of a reduced part of an organism’s metabolism. We propose an approach that combines these two types of modeling to enrich metabolic genome-scale constraint-based models by re-defining their flux bounds. We apply our approach to the constraint-based model ofE. coli, both as a wild-type and when genetically modified to produce citramalate. We show that the enriched model has more realistic reaction flux boundaries. We also resolve a bifurcation of fluxes between growth and citramalate production present in the genetically modified model by fixing the growth rate to the value computed according to kinetic information, enabling us to predict the rate of citramalate production.IMPORTANCEThe investigation addressed in this manuscript is crucial for biotechnology and metabolic engineering, as it enhances the predictive power of metabolic models, which are essential tools in these disciplines. Constraint-based metabolic models, while comprehensive, are limited by their steady-state assumption and difficulty in optimizing competing objectives, whereas kinetic models, though detailed, only cover a small subset of reactions. By integrating these two approaches, our novel methodology refines flux bounds in genome-scale models, leading to more accurate and realistic metabolic predictions. Key highlights include improved predictive accuracy through more realistic flux boundaries, application to both wild-type and genetically modifiedE. colifor citramalate production, successful resolution of the bifurcation between growth and product formation, and broad applicability to other organisms and metabolic engineering projects, paving the way for more efficient bioproduction processes.
Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer
Authors: McPherson Andrew , Vázquez-García Ignacio , Myers Matthew A. , Zatzman Matthew , Al-Rawi Duaa , Weiner Adam , Freeman Samuel , Mohibullah Neeman , Satas Gryte , Williams Marc J. , Ceglia Nicholas , Zhang Allen W. , Li Jun , Lim Jamie L.P. , Wu Michelle , Choi Seongmin , Havasov Eliyahu , Grewal Diljot , Shi Hongyu , Kim Minsoo , Schwarz Roland , Kaufmann Tom , Dinh Khanh Ngoc , Uhlitz Florian , Tran Julie , Wu Yushi , Patel Ruchi , Ramakrishnan Satish , Kim DooA , Clarke Justin , Green Hunter , Ali Emily , DiBona Melody , Varice Nancy , Kundra Ritika , Broach Vance , Gardner Ginger J. , Roche Kara Long , Sonoda Yukio , Zivanovic Oliver , Kim Sarah H. , Grisham Rachel N. , Liu Ying L. , Viale Agnes , Rusk Nicole , Lakhman Yulia , Ellenson Lora H. , Tavaré Simon , Aparicio Samuel , Chi Dennis S. , Aghajanian Carol , Abu-Rustum Nadeem R. , Friedman Claire F. , Zamarin Dmitriy , Weigelt Britta , Bakhoum Samuel F. , Shah Sohrab P. 
Published: July 2024

ABSTRACTWhole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method,doubleTime, we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily ‘active’ mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.
“Micro-offline gains” convey no benefit for motor skill learning
Authors: Das Anwesha , Karagiorgis Alexandros , Diedrichsen Jörn , Stenner Max-Philipp , Azañón Elena 
Published: July 2024

SUMMARYWhile practising a new motor skill, resting for a few seconds can improve performance immediately after the rest. This improvement has been interpreted as rapid offline learning1,2(“micro-offline gains”, MOG), supported by neural replay of the trained movement sequence during rest3. Here, we provide evidence that MOG reflect transient performance benefits, partially mediated by motor planning, and not replay-mediated offline learning. In five experiments, participants trained to produce a sequence of finger movements as many times as possible during fixed-duration practice periods. When participants trained during 10-second practice periods, each followed by a 10-second rest period, they produced more correct keypresses during training than participants who trained without taking breaks. However, this benefit vanished within seconds after the end of training, when both groups performed under comparable conditions, revealing similar levels of skill acquisition. This challenges the idea that MOG reflect offline learning, which, if present, should result in sustained performance benefits, compared to training without breaks. Furthermore, sequence-specific replay was not necessary for MOG, given that we observed persistent MOG when participants produced random sequences that never repeated, preventing any effect of (sequence-specific) replay on the performance. Importantly, we observed diminished MOG when participants could not pre-plan the first few movements of an upcoming practice period. We conclude that “micro-offline gains” represent short-lived performance benefits that are partially driven by motor pre-planning, rather than replay-mediated offline learning.
An extreme mutational hotspot innlpDdepends on transcriptional induction ofrpoS
Authors: Farr Andrew D. , Vasileiou Christina , Lind Peter A. , Rainey Paul B. 
Published: July 2024

AbstractMutation rate varies within and between genomes. Within genomes, tracts of nucleotides, including short sequence repeats and palindromes, can cause localised elevation of mutation rate. Additional mechanisms remain poorly understood. Here we report an instance of extreme mutational bias inPseudomonas fluorescensSBW25 associated with a single base-pair change innlpD.These mutants frequently evolve in static microcosms, and have a cell-chaining (CC) phenotype. Analysis of 153 replicate populations revealed 137 independent instances of a C565T loss-of-function mutation at codon 189 (CAG to TAG (Q189*)). Fitness measures of alternativenlpDmutants showed molecular parallelism to be unconnected to selective advantage. Recognising that transcription can be mutagenic, and that codon 189 overlaps with a predicted promoter (rpoSp) for the adjacent stationary phase sigma factor,rpoS, transcription across this promoter region was measured. This confirmedrpoSpis induced in stationary phase and that C565T mutation caused significant elevation of transcription. The latter provided opportunity to determine the C565T mutation rate using a reporter-gene fused torpoSp. Fluctuation assays demonstrate the C565T mutation rate to be 5,700-fold higher than expected. InPseudomonas, transcription ofrpoSrequires the positive activator PsrA, which we show also holds for SBW25. Fluctuation assays performed in a ΔpsrAbackground showed a 60-fold reduction in mutation rate confirming that the elevated rate of mutation at C565T mutation rate is dependent on induction of transcription. This hotspot suggests a generalisable phenomenon where the induction of transcription causes elevated mutation rates within defining regions of promoters.
Accurate isoform quantification by joint short- and long-read RNA-sequencing
Authors: Apostolides Michael , Choi Benedict , Navickas Albertas , Saberi Ali , Soto Larisa M. , Goodarzi Hani , Najafabadi Hamed S. 
Published: July 2024

AbstractAccurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT’s potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.