bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling mTORopathy-related epilepsy in cultured murine hippocampal neurons using the multi-electrode array
Authors: Heuvelmans Anouk M. , Onori Martina Proietti , Frega Monica , de Hoogen Jeffrey D. , Nel Eveline , Elgersma Ype , van Woerden Geeske M. 
Published: May 2024

ABSTRACTThe mechanistic target of rapamycin complex 1 (mTORC1) signaling pathway is a ubiquitous cellular pathway. mTORopathies, a group of disorders characterized by hyperactivity of the mTORC1 pathway, illustrate the prominent role of the mTOR pathway in disease pathology, often profoundly affecting the central nervous system. One of the most debilitating symptoms of mTORopathies is drug-resistant epilepsy, emphasizing the urgent need for a deeper understanding of disease mechanisms to develop novel anti-epileptic drugs. In this study, we explored the multiwell Multi-electrode array (MEA) system as a tool to identify robust network activity parameters in an approach to model mTORopathy-related epilepsyin vitro. To this extent, we cultured mouse primary hippocampal neurons on the multiwell MEA to identify robust network activity phenotypes in mTORC1-hyperactive neuronal networks. mTOR-hyperactivity was induced either through deletion ofTsc1or overexpression of a constitutively active RHEB variant identified in patients, RHEBp.P37L. mTORC1 dependency of the phenotypes was assessed using rapamycin, and vigabatrin was applied to treat epilepsy-like phenotypes. We show that hyperactivity of the mTORC1 pathway leads to aberrant network activity. In both theTsc1-KO and RHEB-p.P37L models, we identified changes in network synchronicity, rhythmicity, and burst characteristics. The presence of these phenotypes is prevented upon early treatment with the mTORC1-inhibitor rapamycin. Application of rapamycin in mature neuronal cultures could only partially rescue the network activity phenotypes. Additionally, treatment with the anti-epileptic drug vigabatrin reduced network activity and restored burst characteristics. Taken together, we showed that mTORC1-hyperactive neuronal cultures on the multiwell MEA system present reliable network activity phenotypes that can be used as an assay to explore the potency of new drug treatments targeting epilepsy in mTORopathy patients and may give more insights into the pathophysiological mechanisms underlying epilepsy in these patients.ABBREVIATIONSAED, anti-epileptic drug, CoVNIBI, coefficient of variance of NIBI, CTR, control transduced cultures, DIV, days in vitro, DMEM, Dulbecco’s modified Eagle medium, DMSO, dimethyl sulfoxide, GABA, gamma-aminobutyric acidergic, GAPDH, Glyceraldehyde-3-Phosphate Dehydrogenase, iPSC, induced pluripotent stem cell, KO, knock-out, LV, lentivirus, MEA, multi-electrode array, MFR, mean firing rate, mTORC1, mechanistic target of rapamycin complex 1, NB, network burst, NBC, network burst composition, NBD, network burst duration, NBM, neurobasal medium, NBR, network burst rate, NIBI, network interburst interval, NT, non-transduced, RHEB, Ras-homolog enriched in brain, %RS, percentage of random spikes, TBS, tris buffered saline, TSC, Tuberous sclerosis complex, WT, wildtype
Distinct neural computations scale the violation of expected reward and emotion in social transgressions
Authors: Xu Ting , Zhang Lei , Zhou Feng , Fu Kun , Gan Xianyang , Chen Zhiyi , Zhang Ran , Lan Chunmei , Wang Lan , Kendrick Keith M , Yao Dezhong , Becker Benjamin 
Published: May 2024

ABSTRACTTraditional decision-making models conceptualize humans as optimal learners aiming to maximize outcomes by leveraging reward prediction errors (PE). While violated emotional expectations (emotional PEs) have recently been formalized, the underlying neurofunctional basis and whether it differs from reward PEs remain unclear. Using a modified fMRI Ultimatum Game on n=43 participants we modelled reward and emotional PEs in response to unfair offers and subsequent punishment decisions. Computational modelling revealed distinct contributions of reward and emotional PEs to punishment decisions, with reward PE exerting a stronger impact. This process was neurofunctionally dissociable such that (1) reward engaged the dorsomedial prefrontal cortex while emotional experience recruited the anterior insula, (2) multivariate decoding accurately separated reward and emotional PEs. Predictive neural expressions of reward but not emotional PEs in fronto-insular systems predicted neurofunctional and behavioral punishment decisions. Overall, these findings suggest distinct neurocomputational processes underlie reward and emotional PEs which uniquely impact social decisions.
Frequency-dependence in multidimensional diffusion-relaxation correlation MRI of the brain: overfitting or meaningful parameter?
Authors: Yon Maxime , Narvaez Omar , Martin Jan , Jiang Hong , Bernin Diana , Forssell-Aronsson Eva , Laun Frederik , Sierra Alejandra , Topgaard Daniel 
Published: May 2024

AbstractTime- or frequency-dependent (“restricted”) diffusion potentially provides useful information about cellular-scale structures in the brain but is challenging to interpret because of the intravoxel tissue heterogeneity. Frequency-dependence was recently incorporated in a multidimensional diffusion-relaxation correlation MRI framework relying on tensor-valued diffusion encoding at multiple frequencies, as well as variable echo and repetition times, to give model-free characterization of intravoxel heterogeneity by Monte Carlo data inversion into nonparametric distributions of frequency-dependent diffusion tensors and nuclear relaxation rates. While microimaging equipment with high-amplitude magnetic field gradients allows exploration of frequencies from tens to hundreds of Hz, clinical scanners with more moderate gradient capabilities are limited to a narrow frequency range, which may be insufficient to observe effects of restricted diffusion for brain tissues. We here investigate the effects of including or omitting frequency-dependence in the data inversion from isotropic and anisotropic liquids, excised tumor tissue, ex vivo mouse brain, and in vivo human brain. For microimaging measurements covering a wide frequency range, from 35 to 320 Hz atb-values over 4·109sm−2, the inclusion of frequency-dependence drastically reduces the fit residuals and avoids bias in the diffusion metrics for tumor and brain voxels with micrometer-scale structures. Conversely, for the case of in vivo human brain investigated in the narrow frequency range from 5 to 11 Hz atb= 3·109sm−2, analyses with and without inclusion of frequency-dependence yield similar fit residuals and diffusion metrics for all voxels. These results indicate that frequency-dependent inversion may be generally applied to diffusion-relaxation correlation MRI data with and without observable effects of restricted diffusion.
DNA Calorimetric Force Spectroscopy at Single Base Pair Resolution
Authors: Rissone P. , Rico-Pasto M. , Smith S. B. , Ritort F. 
Published: May 2024

AbstractDNA hybridization is a fundamental reaction with wide-ranging applications in biotechnology. The nearest-neighbor (NN) model provides the most reliable description of the energetics of duplex formation. Most DNA thermodynamics studies have been done in melting experiments in bulk, of limited resolution due to ensemble averaging. In contrast, single-molecule methods have reached the maturity to derive DNA thermodynamics with unprecedented accuracy. We combine single-DNA mechanical unzipping experiments using a temperature jump optical trap with machine learning methods and derive the temperature-dependent DNA energy parameters of the NN model. In particular, we measure the previously unknown ten heat-capacity change parametersΔCp, relevant for thermodynamical predictions throughout the DNA stability range. Calorimetric force spectroscopy establishes a groundbreaking methodology to accurately study nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid structures.
Metabolic readouts of tumor instructed normal tissues (TINT) identify aggressive prostate cancer subgroups for tailored therapy
Authors: Dudka Ilona , Wikström Pernilla , Bergh Anders , Gröbner Gerhard 
Published: May 2024

AbstractBackgroundProstate cancer (PC) diagnosis relies on histopathological examination of prostate biopsies, which is restricted by insufficient sampling of all tumors present. Including samples from non-PC but tumor instructed normal tissues (TINT) may increase the diagnostic power by exploring the adaptive responses in benign tissues near tumors.MethodsHere, we applied high-resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) to identify metabolomic biomarkers with high diagnostic value in benign prostate tissues near low/high-grade tumors.ResultsBenign samples near high-grade tumors (B ISUP 3+4) exhibit altered metabolic profiles compared to those close to low-grade tumors (B ISUP 1+2). The levels of six metabolites were significantly different between the two groups; myo-inositol, lysine, serine and combined signal of lysine/leucine/arginine were increased in benign samples near high-grade tumors (B ISUP 3+4) compared to near low-grade tumors (B ISUP 1+2), while levels of ethanolamine and lactate decreased. Additionally, we revealed metabolic differences in non-cancer tissues as a function of their distance to the nearest tumor. Eight metabolites (glutathione, glutamate, combined signal of glutamate/glutamine - glx, glycerol, inosine, ethanolamine, serine and arginine) significantly differentiated between benign tissue located close to the tumor (d ≤ 5 mm) compared to those far away (d ≥ 1 cm).ConclusionsOur HR MAS NMR-based approach identified metabolic signatures in prostate biopsies that reflect the response of benign tissues to the presence of nearby located tumors in the same prostate and confirmed the power of the TINT concept for improved PC diagnostics and understanding of tumor-tissue interactions.
Modelling sex-specific whole-body metabolic responses to feeding and fasting
Authors: Abo Stéphanie M. C. , Layton Anita T. 
Published: May 2024

AbstractMen exhibit a preference for carbohydrate metabolism, whereas women tend to favor lipid metabolism. Significant sex-based differences in energy oxidation are evident across various metabolic states, including fasting and feeding. While some of these differences can be attributed to variations in body composition—such as increased fat mass in women and higher muscle mass in men—there are also inherent disparities in metabolic fluxes. For instance, women exhibit increased rates of lipolysis independent of body composition. However, there remain gaps in our understanding of how sex influences the metabolism of specific organs and how these differences manifest at the systemic level. To address some of these gaps, we developed a sex-specific, whole-body, multi-scale model of metabolism during feeding and fasting. Our model represents healthy young adults (male and female) and integrates cellular metabolism in organs with whole-body responses following various mixed meals, particularly high-carbohydrate and high-fat meals. We explored sex-related variations in metabolic responses during both the absorptive and postabsorptive phases following meals. Our model predicted that sex-related metabolic differences observed at the systemic level are driven by variations in nutrient storage and oxidation patterns in the liver, skeletal muscle, and adipose tissue. We hypothesized that sex differences in hepatic glucose output during short-term fasts are partly influenced by variations in free fatty acids, glycerol, and glycogen handling. We also identified a candidate mechanism, possibly more prevalent in the female liver, where lipids are redirected toward carbohydrate metabolism to support hepatic glucose production. Integrating sex-specific data and parameters into multi-scale frameworks holds promise for enhancing our understanding of human metabolism and its modulation by sex.Author summaryMen and women exhibit different metabolic preferences, with men favoring carbohydrate metabolism and women favoring lipid metabolism. These differences impact energy usage during fasting and feeding, influenced by body composition and inherent metabolic variations. However, there remain gaps in our understanding of how sex influences the metabolism of specific organs and how these differences manifest at the systemic level. To address these gaps, we developed a sex-specific whole-body metabolic model, representing healthy young adults. Our model explores how men and women metabolize mixed meals, especially high-carbohydrate and high-fat meals. We found that sex-related metabolic variations arise from differences in nutrient storage and utilization in key organs like the liver, muscles, and fat tissue. We propose that variations in free fatty acids and glycogen handling contribute to differences in hepatic glucose output between men and women during short-term fasting. Integrating sex-specific data into metabolic models can enhance our understanding of human metabolism and its modulation by sex.
Predictive modeling of microbial data with interaction effects
Authors: Stadler Mara , Bien Jacob , Müller Christian L. 
Published: May 2024

AbstractMicrobial interactions are of fundamental importance for the functioning and the maintenance of microbial communities. Deciphering these interactions from observational data or controlled lab experiments remains a formidable challenge due to their context-dependent nature, i.e., their dependence on (a)biotic factors, host characteristics, and overall community composition. Here, we present a statistical regression framework for microbial data that allows the inclusion and parsimonious estimation of species interaction effects for an outcome of interest. We adapt the penalized quadratic interaction model to accommodate common microbial data types as predictors, including microbial presence-absence data, relative (or compositional) abundance data from microbiome surveys, and quantitative (absolute abundance) microbiome data. We study the effect of including hierarchical interaction constraints and stability-based model selection on model performance and propose novel interaction model formulations for compositional data. To illustrate our framework’s versatility, we consider prediction tasks across a wide range of microbial datasets and ecosystems, including metabolite production in model communities in designed experiments and environmental covariate prediction from marine microbiome data. While we generally observe superior predictive performance of our interaction models, we also assess limits of these models in presence of extreme data sparsity and with respect to data type. On a large-scale gut microbiome cohort data, we identify sparse family-level interaction models that accurately predict the abundance of antimicrobial resistance genes, enabling the formulation of novel biological hypotheses about microbial community interactions and antimicrobial resistance.
Neural bases of proactive and predictive processing of meaningful sub-word units in speech comprehension
Authors: Matar Suhail , Marantz Alec 
Published: May 2024

AbstractTo comprehend speech, human brains identify meaningful units in the speech stream. But whereas the English ‘She believed him.’ has 3 words, the Arabic equivalent ‘ṣaddaqathu.’ is a single word with 3 meaningful sub-word units, called morphemes: a verb stem (‘ṣaddaqa’), a subject suffix (‘-t-’), and a direct object pronoun (‘-hu’). It remains unclear whether and how the brain processes morphemes, above and beyond other language units, during speech comprehension. Here, we propose and test hierarchically-nested encoding models of speech comprehension: a NAÏVE model with word-, syllable-, and sound-level information; a BOTTOM-UP model with additional morpheme boundary information; and PREDICTIVE models that process morphemes before these boundaries. We recorded magnetoencephalography (MEG) data as participants listened to Arabic sentences like ‘ṣaddaqathu.’. A temporal response function (TRF) analysis revealed that in temporal and left inferior frontal regions PREDICTIVE models outperform the BOTTOM-UP model, which outperforms the NAÏVE model. Moreover, verb stems were either length-AMBIGUOUS (e.g., ‘ṣaddaqa’ could initially be mistaken for the shorter stem ‘ṣadda’=‘blocked’) or length-UNAMBIGUOUS (e.g., ‘qayyama’=‘evaluated’ cannot be mistaken for a shorter stem), but shared a uniqueness point, at which stem identity is fully disambiguated. Evoked analyses revealed differences between conditions before the uniqueness point, suggesting that, rather than await disambiguation, the brain employs PROACTIVE PREDICTIVE strategies, processing the accumulated input as soon as any possible stem is identifiable, even if not unique. These findings highlight the role of morpheme processing in speech comprehension, and the importance of including morpheme-level information in neural and computational models of speech comprehension.Significance statementMany leading models of speech comprehension include information about words, syllables and sounds. But languages vary considerably in the amount of meaning packed into word units. This work proposes speech comprehension models with information about meaningful sub-word units, called morphemes (e.g., ‘bake-’ and ‘-ing’ in ‘baking’), and shows that they explain significantly more neural activity than models without morpheme information. We also show how the brain predictively processes morphemic information. These findings highlight the role of morphemes in speech comprehension and emphasize the contributions of morpheme-level information-theoretic metrics, like surprisal and entropy. Our models can be used to update current neural, cognitive, and computational models of speech comprehension, and constitute a step towards refining those models for naturalistic, connected speech.
Oligomerization and cellular localization of SLC26A11
Authors: Bungert-Plümke Stefanie , Guzman Raul E. , Fahlke Christoph 
Published: May 2024

AbstractThe solute carrier family 26 (SLC26) encompasses multifunctional anion exchangers in all kingdoms of life. SLC26 proteins are known to assemble as dimers, and co-expression of multiple isoforms in certain cells raises the question whether different SLC26s can assemble into hetero-dimers. We focused on SLC26A11, a broadly expressed isoform that differs from other isoforms in its subcellular localization. Whereas the vast majority of SLC26-FP fusion proteins, i.e. SLC26A1, SLC26A2, SlC26A3, SLC26A4/pendrin, SLC26A5/prestin, SLC26A6, SlC26A7, and SLC26A9, localize to the surface membrane of transfected mammalian cells, we found exclusive lysosomal localization of SLC26A11. Renal collecting duct intercalated cells express SLC26A11 together with SLC26A4/pendrin and SLC26A7, and we therefore tested whether heterodimerization between these transporters might result in SLC26 transporter re-localization. Neither in HEK293T nor in immortalized intercalated cells co-expressing SLC26A11 with SLC26A4/pendrin or with SLC26A7, changes of SLC26A11 localization were observed. Moreover, native gel electrophoresis did not provide any evidence for heterodimerization of these isoforms. We next tested heterodimerization of SLC26A11 with SLC26A1, SLC26A2, SLC26A6 or SLC26A9 via co-expression in HEK293T cells and confocal imaging. For all combinations, no changes in subcellular distribution were observed. We conclude that SLC26A11 does not heterodimerize with other SLC26 proteins, and that heterodimerization does not target SLC26A11 to cellular surface membranes.
Crystallographic fragment screen of Enterovirus D68 3C protease and iterative design of lead-like compounds using structure-guided expansions
Authors: Lithgo Ryan M. , Tomlinson Charles W.E. , Fairhead Michael , Winokan Max , Thompson Warren , Wild Conor , Aschenbrenner Jasmin Cara , Balcomb Blake H. , Marples Peter G. , Chandran Anu V , Golding Mathew , Koekemoer Lizbe , Williams Eleanor P. , Wang SiYl , Ni Xiaomin , MacLean Elizabeth , Giroud Charline , Zarganes-Tzitzikas Tryfon , Godoy Andre Schutzer , Xavier Mary-Ann , Walsh Martin , Fearon Daren , von Delft Frank 
Published: May 2024

AbstractThe development of effective broad-spectrum antivirals forms an important part of preparing for future pandemics. One cause for concern is the currently emerging pathogenEnterovirus D68(EV-D68) which primarily spreads through respiratory routes causing mostly mild to severe respiratory illness but, in severe cases, acute flaccid myelitis. The 3C protease of EV-D68 (3Cpro) is a potential target for the development of antiviral drugs due to its essential role in the viral life cycle and high sequence conservation amongst family members. In this study, we describe the identification of fragments which bind to3Cprousing crystallographic screening and the expansion of these into more lead-like compounds. The hits revealed interesting directions for hit-to-lead progression, specifically the importance of the pocket occupied by the conserved glutamine sidechain of the substrates and the interactions formed. Additionally, two pockets could be joined by not following the backbone of the native substrates, thus circumventing the screening issues arising from the flexibility of the catalytic triad. These observations of the novel binding modes of the chemical matter found by this screen can help shape future drug design campaigns against 3C proteases.
“A Low-Cost Microfluidics System for Light Microscopy Experiments”
Authors: Harrison Logan M. , Brisard Benjamin M. , Cashwell Kylie D. , Mulkey Aaron L. , Schmidt Cameron A. 
Published: May 2024

AbstractMicrofluidics devices are powerful tools for studying dynamic processes in live cells, especially when used in conjunction with light microscopy. There are many applications of microfluidics devices including recording dynamic cellular responses to small molecules or other chemical conditions in perfused media, monitoring cell migration in constrained spaces, or collecting media perfusate for the study of secreted compounds in response to experimental inputs/manipulations. Here we describe a configurable low-cost (channel-based) microfluidics platform for live-cell microscopy, intended to be useful for experiments that require more precision/flexibility than simple rubber spacers, but less precision than molded elastomer-based platforms. The materials are widely commercially available, low-cost, and device assembly takes only minutes.
New insights into the echinocandin resistance inCandidaspp. in the clinical setting
Authors: Gedeon Antoine , Kalboussi Yasmine , Bigot Jeanne , Le Vicky , Dannaoui Eric , Vellaissamy Sandra , Antignac Marie , Petrella Stéphanie , Hennequin Christophe , Guitard Juliette 
Published: May 2024

AbstractDespite a huge consumption of echinocandins, the emergence of resistance inCandida spphas remained overall limited. Here, we depicted the epidemiology ofCandidaspp in our center face to the echinocandins’ consumption. We postulate new hypotheses that may explain the shaping of candines resistance in the clinical setting. Epidemiology ofCandidainfections and echinocandin consumption were evaluated in our center over 12 years (2006-2018). Glucan synthase genes (fks1, fks2)were sequenced. Thein vitrofitness was assessed for couple of isogenic strains, of which one was resistant. Finally, the modelling ofCandidaFKS proteins was realized. Despite a three-fold increase in echinocandin consumption, no significant emergence of resistance was observed. InCandida albicans, fks1mutations affect the three-dimensional conformation of the glucan synthase, which may result in altered export of the glucan chain, which could explain the observed reduced fitness. In contrast, cross-complementation between FKS1 and FKS2 inNakaseomyces glabratusmight circumvent the negative impact on fitness.ConclusionsOur results support the fact thatN. glabratus is more prone to the spread of echinocandin-resistant strains. The link between mutations in the glucan synthase and fitness ofCandidastrains might explain the difference in the species-specific emergence of echinocandins-resistant strains.