bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systematic evaluation of protein-small molecule hybrids on the yeast surface
Authors: Huang Manjie , Rueda-Garcia Marina , Harthorn Abbigael , Hackel Benjamin J. , Van Deventer James A. 
Published: May 2023

AbstractProtein-small molecule hybrids are structures that have the potential to combine the inhibitory properties of small molecules and the specificities of binding proteins. However, achieving such synergies is a substantial engineering challenge, with fundamental principles yet to be elucidated. Recent work has demonstrated the power of yeast display-based discovery of hybrids using a combination of fibronectin binding domains and thiol-mediated conjugations to introduce small molecule warheads. Here, we systematically study the effects of expanding the chemical diversity of these hybrids on the yeast surface, investigating a combinatorial set of fibronectins, noncanonical amino acid (ncAA) substitutions, and small molecule pharmacophores. Our results show that previously discovered thiol-fibronectin hybrids are generally tolerant of a range of ncAA substitutions and retain binding to carbonic anhydrases following click chemistry-mediated assembly of hybrids with diverse linker structures. Most surprisingly, we identified several cases where replacement of a potent acetazolamide warhead with a substantially weaker benzenesulfonamide warhead still resulted in the assembly of functional hybrids. In addition to these unexpected findings, we expanded the throughput of our system by validating a 96-well plate-based format to produce yeast-displayed hybrid conjugates in parallel. These efficient explorations of hybrid chemical diversity demonstrate that there are abundant opportunities to expand the functions of protein-small molecule hybrids and elucidate principles that dictate their efficient discovery and design.
The impact of host heterogeneity on pathogen evolution depends on spatial structure
Authors: Visher Elisa , Ali Anisha , Barajas Jonathan , Masud Sehar , McBride Annika , Ramos Edwin , Sui Melissa , Villalobos-Heredia Cristina , Walzer Natalie , White P. Signe , Boots Mike 
Published: May 2023

ABSTRACTHost diversity and spatial structure both separately impact parasite evolution and may also interact when space structures host heterogeneity and populations must adapt to both selection pressures. Here, we experimentally evolve granulosis virus in microcosms of itsPlodia interpunctella(Indian meal moth) host that vary in spatial structure and host genetic diversity. We do this by manipulating food viscosity (spatial structure) and adding either one (homogeneous) or two (heterogeneous) host genotypes to the microcosm. Spatial structure therefore bot generally structures of infectious contacts and decreases the rate of contact between genotypes. We preserve spatial structure across passages and assay virus from different positions on the two host genotypes. We find that virus evolves specific interactions with its locally familiar host genotype, but that the impact of specialization on evolved phenotypes depends on the spatial structure. In spatially structured treatments, exploitation rates are higher on the familiar host, while, in well-mixed treatments, they are lower. We also find that virus can specialize to their locally familiar host in heterogeneous microcosms when spatial structure lowers contact rates between hosts. These results demonstrate that host genetic diversity and spatial structure can interact to reshape the evolution of pathogen host range. [197/200]
Defining Mitochondrial Cristae Morphology Changes Induced by Aging in Brown Adipose Tissue
Authors: Crabtree Amber , Neikirk Kit , Marshall Andrea G. , Vang Larry , Whiteside Aaron J. , Williams Qiana , Altamura Christopher T. , Owens Trinity Celeste , Stephens Dominique , Shao Bryanna , Koh Alice , Killion Mason , Lopez Edgar Garza , Lam Jacob , Rodriguez Ben , Mungai Margaret , Stanley Jade , Dean E. Danielle , Koh Ho-Jin , Gaddy Jennifer A. , Scudese Estevão , Sweetwyne Mariya , Davis Jamaine , Zaganjor Elma , Murray Sandra A. , Katti Prasanna , Damo Steven M. , Vue Zer , Hinton Antentor 
Published: May 2023

ABSTRACTMitochondria are required for energy production and even give brown adipose tissue (BAT) its characteristic color due to their high iron content and abundance. The physiological function and bioenergetic capacity of mitochondria are connected to the structure, folding, and organization of its inner-membrane cristae. During the aging process, mitochondrial dysfunction is observed, and the regulatory balance of mitochondrial dynamics is often disrupted, leading to increased mitochondrial fragmentation in aging cells. Therefore, we hypothesized that significant morphological changes in BAT mitochondria and cristae would be present with aging. We developed a quantitative three-dimensional (3D) electron microscopy approach to map cristae network organization in mouse BAT to test this hypothesis. Using this methodology, we investigated the 3D morphology of mitochondrial cristae in adult (3-month) and aged (2-year) murine BAT tissue via serial block face-scanning electron microscopy (SBF-SEM) and 3D reconstruction software for manual segmentation, analysis, and quantification. Upon investigation, we found increases in mitochondrial volume, surface area, and complexity and decreased sphericity in aged BAT, alongside significant decreases in cristae volume, area, perimeter, and score. Overall, these data define the nature of the mitochondrial structure in murine BAT across aging.Abstract FigureGraphical Abstract:Overview of serial block facing-scanning electron microscopy (SBF-SEM) workflow, data segmentation, and 3D analysis of mitochondria using Amira software for murine interscapular BAT.
Regulation of shelterin proteins TERF2IP and TRF2 by H3K4me3-p65 axis drives hyperglycemia dependent endothelial senescence
Authors: Thakar Sumukh , Bhattacharya Ritobrata , Katakia Yash T , Chakraborty Srinjoy , Ramakrishnan S , Thakkar Niyati Pandya , Dey Smita , Chowdhury Shibasish , Majumder Syamantak 
Published: May 2023

AbstractBackgroundEndothelial senescence has been linked to several cardiovascular diseases. Dysregulation of proteins of the shelterin complex including TRF2 and TERF2IP causes senescence as it hampers DNA repair and cell proliferation. However, whether exposure to hyperglycemia interplays with proteins of the shelterin complex thus further dictates the senescent phenotype of endothelial cells (EC) remain to be explored.Approach and ResultsIn this study, we observed elevated levels of p21 and p53 in endothelial cells upon exposure to intermittent hyperglycemia. We also noted hyperglycemia exposure increased the levels of TERF2IP and TRF2, part of the shelterin complex. No change in the level of TRF1 and TPP1 were detected. Furthermore, a robust induction was detected in p65 level upon intermittent hyperglycemia challenge. ChIP-qPCR analysis revealed enhanced H3K4me3 enrichment in the promoter regions of p65, TERF2IP and TRF2. Inhibition of catalysis of H3K4me3 either by pharmacological inhibitor or siRNA-mediated knockdown of MLL2 attenuated increase in p65, TERF2IP and TRF2 levels including reversal of senescence markers p53 and p21. Interestingly, pharmacological inhibition of NF-κB signaling also diminished abrupt increase in TERF2IP and TRF2 levels thus further reversed intermittent hyperglycemia-induced p53 and p21 levels. More importantly, co-immunoprecipitation and co-localization analysis revealed an interaction between nuclear p65 and MLL2 in EC stimulated with hyperglycemia. Further knockdown of either TERF2IP or TRF2 impaired intermittent hyperglycemia-induced p53 and p21 expression and associated endothelial senescence.ConclusionOverall, the present study describes an interplay of epigenetics in defining NF-κB signaling and shelterin proteins’ expression which further govern the biochemical and functional state of endothelial senescence in hyperglycemic milieu.
Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development
Authors: Natesan Gunalan , Hamilton Timothy , Deeds Eric J. , Shah Pavak K. 
Published: May 2023

AbstractHigh throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treatedCaenorhabditis elegansembryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.
In Silico Investigations of Adaptive Therapy Using Two Cytotoxic or Two Cytostatic Drugs
Authors: Saha Daniel K. , Anderson Alexander R. A. , Cisneros Luis , Maley Carlo C. 
Published: May 2023

AbstractWhile the dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression) involves adjusting drug dosages when the tumor burden changes, the fixed-dose (FD) protocols involves administering a specific, constant dosage of the drug only when the tumor is growing (Dose-Skipping) or when the absolute tumor burden is above the baseline level until it reduces to a certain percentage of the baseline (Intermittent). Moreover, two different drugs can be administered simultaneously (cocktail), or the drugs can be switched such that only one drug is applied at a given time (ping-pong), either every cycle (ping-pong alternate every cycle) or when the tumor grows (ping-pong on progression). The dose modulation protocols work well when treated with two cytotoxic drugs, however, the ping-pong protocols (DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression, FD Ping-Pong Intermittent) work well when treated with two cytostatic drugs. In general, adaptive therapy, using either two cytotoxic or two cytostatic drugs works best under conditions of high competition, such as high fitness cost, high replacement rates, and high turnover, although treatment using two cytostatic drugs works best under low turnover in many cases. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected, and it is best to pause treatment sooner rather than later when the tumor is shrinking. Adaptive therapy works best when an intermediate level of drug dosage is used, and both treatment with too little or too much drug leads to poor survival outcome.
Frequency and Phase Correction of GABA-Edited Magnetic Resonance Spectroscopy using Complex-Valued Convolutional Neural Networks
Authors: Bugler Hanna , Berto Rodrigo Pommot , Souza Roberto , Harris Ashley D. 
Published: May 2023

ABSTRACTPurposeTo determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in Convolutional Neural Networks (CNNs) for frequency and phase correction (FPC) of GABA-edited Magnetic Resonance Spectroscopy (MRS) data.MethodsAn ablation study was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using GABA’s signal-to-noise ratio and linewidth, Choline artifact, and by analyzing the reconstructed final difference spectrum. Statistical significance and effect size were assessed using the Wilcoxon signed rank test and Cohen’s d respectively.ResultsThe ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with real (conventional) convolutions was most effective for FPC. For the comparative study, the simulated data test set showed that our CNN model that received complex-valued inputs with real convolutions outperformed models it was compared against with a lower mean absolute error (p<0.05). For the in vivo data test set, our model performed similarly to other DL FPC models.ConclusionOur results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and real-valued convolutions. This model outperformed existing DL models on simulated data and performed similarly on in vivo data.
Considering Zeros in Single Cell Sequencing Data Correlation Analysis
Authors: Cai Guoshuai , Yu Xuanxuan , Xiao Feifei 
Published: May 2023

AbstractSingle-cell sequencing technology has enabled correlation analysis of genomic features at the cellular level. However, high levels of noise and sparsity in single-cell sequencing data make accurate assessment of correlations challenging. This study provides a toolkit, SCSC (https://github.com/thecailab/SCSC), for the estimation of correlation coefficients in single-cell sequencing data. It comprehensively assessed four strategies (classical, non-zero, dropout-weighted, imputation) and the impact of data features in various simulated scenarios. The study found that filtering zeros significantly improves estimation accuracy, and further improvement can be achieved by considering the drop-out probability. In addition, the study also identified data features including expression level, library size, and biological variations that affect correlation estimation.
AnnoPRO: an Innovative Strategy for Protein Function Annotation based on Image-like Protein Representation and Multimodal Deep Learning
Authors: Zheng Lingyan , Shi Shuiyang , Fang Pan , Zhang Hongning , Pan Ziqi , Huang Shijie , Xia Weiqi , Li Honglin , Zeng Zhenyu , Zhang Shun , Chen Yuzong , Lu Mingkun , Li Zhaorong , Zhu Feng 
Published: May 2023

AbstractProtein function annotation has been one of the longstanding issues, which is key for discovering drug targets and understanding physiological or pathological process. A variety of computational methods have therefore been constructed to facilitate the research developments in this particular direction. However, the annotation of protein function based on computational methods has been suffering from the serious “long-tail problem”, and it remains extremely challenging for existing methods to improve the prediction accuracies for protein families intail label levels. In this study, an innovative strategy, entitled ‘AnnoPRO’, for protein function annotation was thus constructed.First, a novel method enabling image-like protein representations was proposed. This method is unique in capturing the intrinsic correlations among protein features, which can greatly favor the application of thestate-of-the-artdeep learning methods popular in image classification.Second, a multimodal framework integrating multichannel convolutional neural network and long short-term memory neural network was constructed to realize a deep learning-based protein functional annotation. Since this framework was inspired by a reputable method used in image classification for dealing with its ‘long-tail problem’, ourAnnoPROwas expected to significantly improve the annotation performance of the protein families intail label level. Multiple case studies based on benchmark were also conducted, which confirmed the superior performance ofAnnoPROamong the existing methods. All source codes and models ofAnnoPROwere freely available to all users athttps://github.com/idrblab/AnnoPRO, and would be essential complement to existing methods.
Models to Estimate Genetic Gain of Soybean Seed Yield from Annual Multi-Environment Field Trials
Authors: Krause Matheus D. , Piepho Hans-Peter , Dias Kaio O. G. , Singh Asheesh K. , Beavis William D. 
Published: May 2023

1AbstractGenetic improvements of discrete characteristics such as flower color, the genetic improvements are obvious and easy to demonstrate; however, for characteristics that are measured on continuous scales, the genetic contributions are incremental and less obvious. Reliable and accurate methods are required to disentangle the confounding genetic and non-genetic components of quantitative traits. Stochastic simulations of soybean (Glycine max (L.) Merr.) breeding programs were performed to evaluate models to estimate the realized genetic gain (RGG) from 30 years of multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Results indicated all estimation models were biased. Covariance modeling as well as direct versus indirect estimation resulted in substantial differences in RGG estimation. Although there were no unbiased models, the three best-performing models resulted in an average bias of ±7.41 kg/ha−1/yr−1(±0.11 bu/ac−1/yr−1). Rather than relying on a single model to estimate RGG, we recommend the application of multiple models and consider the range of the estimated values. Further, based on our simulations parameters, we do not think it is appropriate to use any single models to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America from 1989 to 2019, the range of estimated RGG values was from 18.16 to 39.68 kg/ha−1/yr−1(0.27 to 0.59 bu/ac−1/yr−1).
A combined lipidomic and proteomic profiling ofArabidopsis thalianaplasma membrane
Authors: Bahammou Delphine , Recorbet Ghislaine , Cassim Adiilah Mamode , Robert Franck , Balliau Thierry , Delft Pierre Van , Haddad Youcef , Mongrand Sébastien , Fouillen Laetitia , Simon-Plas Françoise 
Published: May 2023

AbstractThe plant plasma membrane (PM) plays a key role in nutrition, cell homeostasis, perception of environmental signals, and set-up of appropriate adaptive responses. An exhaustive and quantitative description of the whole set of lipids and proteins constituting the PM is thus necessary to understand how the way these components, are organized and interact together, allow to fulfill such essential physiological functions. Here we provide by state-of-the-art approaches the first combined reference of the plant PM lipidome and proteome fromArabidopsis thalianasuspension cell culture. We identified a reproducible core set of 2,165 proteins (406 of which had not been shown associated to PM previously), which is by far the largest set of available data concerning the plant PM proteome. Using the same samples, we combined lipidomic approaches, allowing the identification and quantification of an unprecedented repertoire of 405 molecular species of lipids. We showed that the different classes of lipids (sterols, phospholipids, and sphingolipids) are present in similar proportions in the plant PM. Within each lipid class, the precise amount of each lipid family and the relative proportion of each molecular species were further determined, allowing us to establish the complete lipidome of Arabidopsis PM, and highlighting specific characteristics of the different molecular species of lipids (for instance fatty acyl chain length and saturation according to the polar head). Results obtained are consistent with the plant PM being an ordered mosaic of domains and point to a finely tuned adjustment of the molecular characteristics of lipids and proteins. More than a hundred proteins related to lipid metabolism, transport or signaling have been identified and put in perspective of the lipids with which they are associated. All these results provide an overall view of both the organization and the functioning of the PM.
Post-training sleep modulates motor adaptation and task-related beta oscillations
Authors: Ameen Mohamed S. , Petzka Marit , Peigneux Philippe , Hoedlmoser Kerstin 
Published: May 2023

AbstractMotor adaptation reflects the ability of the brain’s sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatized motor skill, i.e., typing. We hypothesized that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography (EEG). Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power (a) during mirrored typing as compared to regular typing, and (b) in the post-sleep vs. the pre-sleep mirrored typing sessions. Furthermore, the slope of the EEG signal, a measure of aperiodic brain activity, decreased during mirrored as compared to regular typing. Changes in the EEG spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity mechanisms supporting adaptive motor functions.