bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids
Authors: Lumibao Jan C. , Okhovat Shira R. , Peck Kristina L. , Lin Xiaoxue , Lande Kathryn , Zou Jingjing , Engle Dannielle D. 
Published: January 2023

AbstractThe use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance (pharmacotyping) is a promising precision medicine approach. The potential of this approach to inform clinical decisions is now being tested in several large multi-institutional clinical trials. PDOs are cultivated in extracellular matrix from basement membrane extracts (BMEs) that are most commonly acquired commercially. Each clinical site utilizes distinct BME lots and may be restricted due to the availability of commercial BME sources. However, the impact of different sources and lots of BMEs on organoid drug response is unknown. Here, we tested the impact of BME source and lot on proliferation, chemotherapy and targeted therapy drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel (Corning) compared to Cultrex (RnD) and UltiMatrix (RnD). However, we observed no substantial impact on drug response when oragnoids were cultured in Matrigel, Cultrex, or UltiMatrix. We also did not observe major shifts in gene expression across the different BME sources, and PDOs maintained their Classical or Basal-like designation. Overall, we find that BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves and drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.
Time to degree, identity, and grant writing: Lessons learned from a mixed-methods longitudinal case study
Authors: Alvero AJ , Peña Courtney , Moore Amber R , Luqueno Leslie , Barron Cisco B. , Steele Latishya , Eberle Stevie , Botham Crystal M. 
Published: January 2023

AbstractTime to degree completion is an important metric of academic progress and success for doctoral students. It is also a common way for educational stakeholders to compare programs even if the content of the degree programs varies. But what types of behaviors and experiences are associated with faster times to degree? In this education article, we examine the relationship between receiving competitive research awards (e.g. grant writing) and time to degree for PhD students. We organize our analyses by student identities, specifically gender and underrepresented minority (URM) status, to examine differences in time to degree based on student demographics. Our dataset included students that graduated between academic years 2008-09 through 2018-19. We also interviewed students currently enrolled in these same programs. We find that URM women who won competitive research awards graduate faster than all other students who also won awards but also report lower levels of advisor support. We also find that URM women and all URM students tended to graduate faster compared to other groups of students who did not win awards. Students who reported feeling supported by their advisors, most of which reflected hands-on guidance through the writing process, in the interviews were more likely to apply for grants. Combined, these results highlight that writing grants and specific types of advisor support may influence faster times to degree for bioscience PhD students. This study suggests similar introspective analyses at other institutions and databases are needed.
Autoinhibited kinesin-1 adopts a hierarchical folding pattern
Authors: Tan Zhenyu , Yue Yang , Leprevost Felipe da Veiga , Haynes Sarah E. , Basrur Venkatesha , Nesvizhskii Alexey I. , Verhey Kristen J. , Cianfrocco Michael A. 
Published: January 2023

AbstractConventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine cross-linking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer [kinesin-1 heavy chain (KHC)] and kinesin-1 heterotetramer [KHC bound to kinesin light chain 1 (KLC1)]. Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation.
Ultra-Fast Bioorthogonal Spin-Labeling and Distance Measurements in Mammalian Cells Using Small, Genetically Encoded Tetrazine Amino Acids
Authors: Jana Subhashis , Evans Eric G. B. , Jang Hyo Sang , Zhang Shuyang , Zhang Hui , Rajca Andrzej , Gordon Sharona E. , Zagotta William N. , Stoll Stefan , Mehl Ryan A. 
Published: January 2023

AbstractStudying protein structures and dynamics directly in the cellular environments in which they function is essential to fully understand the molecular mechanisms underlying cellular processes. Site-directed spin-labeling (SDSL)—in combination with double electron–electron resonance (DEER) spectroscopy—has emerged as a powerful technique for determining both the structural states and the conformational equilibria of biomacromolecules. In-cell DEER spectroscopy on proteins in mammalian cells has thus far not been possible due to the notable challenges of spin-labeling in live cells. In-cell SDSL requires exquisite biorthogonality, high labeling reaction rates and low background signal from unreacted residual spin label. While the bioorthogonal reaction must be highly specific and proceed under physiological conditions, many spin labels display time-dependent instability in the reducing cellular environment. Additionally, high concentrations of spin label can be toxic. Thus, an exceptionally fast bioorthogonal reaction is required that can allow for complete labeling with low concentrations of spin-label prior to loss of signal. Here we utilized genetic code expansion to site-specifically encode a novel family of small, tetrazine-bearing non-canonical amino acids (Tet-v4.0) at multiple sites in green fluorescent protein (GFP) and maltose binding protein (MBP) expressed both inE. coliand in human HEK293T cells. We achieved specific and quantitative spin-labeling of Tet-v4.0-containing proteins by developing a series of strainedtrans-cyclooctene (sTCO)-functionalized nitroxides—including agem-diethyl-substituted nitroxide with enhanced stability in cells—with rate constants that can exceed 106M−1s−1. The remarkable speed of the Tet-v4.0/sTCO reaction allowed efficient spin-labeling of proteins in live HEK293T cells within minutes, requiring only sub-micromolar concentrations of sTCO–nitroxide added directly to the culture medium. DEER recorded from intact cells revealed distance distributions in good agreement with those measured from proteins purified and labeledin vitro. Furthermore, DEER was able to resolve the maltose-dependent conformational change of Tet-v4.0-incorporated and spin-labeled MBPin vitroand successfully discerned the conformational state of MBP within HEK293T cells. We anticipate the exceptional reaction rates of this system, combined with the relatively short and rigid side chains of the resulting spin labels, will enable structure/function studies of proteins directly in cells, without any requirements for protein purification.TOC
Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction
Authors: Song Inuk , Lee Tae-Ho 
Published: January 2023

AbstractThe brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE;N= 1249), Schizophrenia disorder (COBRE;N= 145), and typical development (NKI;N= 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.
Top-down Proteomics of Myosin Light Chain Isoforms Define Chamber-Specific Expression in the Human Heart
Authors: Bayne Elizabeth F. , Rossler Kalina J. , Gregorich Zachery R. , Aballo Timothy J. , Roberts David S. , Chapman Emily A. , Guo Wei , Ralphe J. Carter , Kamp Timothy J. , Ge Ying 
Published: January 2023

AbstractMyosin functions as the “molecular motor” of the sarcomere and generates the contractile force necessary for cardiac muscle contraction. Myosin light chains 1 and 2 (MLC-1 and -2) play important functional roles in regulating the structure of the hexameric myosin molecule. Each of these light chains has an ‘atrial’ and ‘ventricular’ isoform, so called because they are believed to exhibit chamber-restricted expression in the heart. However, recently the chamber-specific expression of MLC isoforms in the human heart has been questioned. Herein, we analyzed the expression of MLC-1 and -2 atrial and ventricular isoforms in each of the four cardiac chambers in adult non-failing donor hearts using top-down mass spectrometry (MS)-based proteomics. Strikingly, we detected an isoform thought to be ventricular, MLC-2v, in the atria and confirmed the protein sequence using tandem MS (MS/MS). For the first time, a putative deamidation post-translation modification (PTM) located on MLC-2v in atrial tissue was localized to amino acid N13. MLC-1v and MLC-2a were the only MLC isoforms exhibiting chamber-restricted expression patterns across all donor hearts. Importantly, our results unambiguously show that MLC-1v, not MLC-2v, is ventricle-specific in adult human hearts. Overall, top-down proteomics allowed us an unbiased analysis of MLC isoform expression throughout the human heart, uncovering previously unexpected isoform expression patterns and PTMs.
Emergence of distributed working memory in a human brain network model
Authors: Feng Mengli , Bandyopadhyay Abhirup , Mejias Jorge F. 
Published: January 2023

AbstractWorking memory is a fundamental cognitive function which allows to transiently store and manipulate relevant information in memory. While it has been traditionally linked to activity in specific prefrontal cortical areas, recent electrophysiological and imaging evidence has shown co-occurrent activities in different brain regions during working memory. To dissect the mechanisms behind the emergence of such distributed working memory activity in the human brain, we built and analyzed a detailed, data-constrained model of the human brain combining multiple sources of large-scale brain data. The model reveals the emergence of distributed working memory patterns which rely on long-range synaptic projections, differing from classical models where working memory emerges due to local recurrent connectivity. When compared to existing evidence regarding areas linked to working memory, our model explains almost 80% of the observed variability across brain areas. Our results predict important differences between the distributed working memory in human and non-human primates, and propose a potential solution for the debate regarding the role of early visual areas in working memory encoding. This work provides a novel approach to simulate the human brain which combines the richness of large-scale brain dynamics with the insight of mechanistic models of cognitive functions.
A unified hypothesis-free feature extraction framework for diverse epigenomic data
Authors: Balcı Ali Tuğrul , Chikina Maria 
Published: January 2023

AbstractMotivationEpigenetic assays using next-generation sequencing (NGS) have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data represented by nucleotide resolution signal tracks. The signal strength at a given nucleotide is subject to numerous sources of technical and biological noise and thus conveys limited information about the underlying biological state. In order to draw biological conclusions, data is typically summarized into higher order patterns. Numerous specialized algorithms for summarizing epigenetic signal have been proposed and include methods for peak calling or finding differentially methylated regions. A key unifying principle underlying these approaches is that they all leverage the strong prior that signal must be locally consistent.ResultsWe proposeL0segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources.L0serves to both compress and smooth the input signal by approximating it as piece-wise constant. We implement a highly scalableL0segmentation with additional loss functions designed for NGS epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that theL0segmentation approach retains the salient features of the data yet can identify subtle features, such as transcription end sites, missed by other analytic approaches.AvailabilityOur approach is implemented as an R package “l01segmentation” with a C++ backend. Available athttps://github.com/boooooogey/l01segmentation.
Genetic perturbation of mitochondrial function reveals functional role for specific mitonuclear genes, metabolites and pathways that regulate lifespan
Authors: Phua Cheryl Zi Jin , Zhao Xiaqing , Turcios-Hernandez Lesly , McKernan Morrigan , Abyadeh Morteza , Ma Siming , Promislow Daniel , Kaeberlein Matt , Kaya Alaattin 
Published: January 2023

AbstractAltered mitochondrial function is tightly linked to lifespan regulation, but underlying mechanisms remain unclear. Here, we report the chronological and replicative lifespan variation across 168 yeast knock-out strains, each lacking a single nuclear-coded mitochondrial gene, including 144 genes with human homologs, many associated with diseases. We dissected the signatures of observed lifespan differences by analyzing profiles of each strain’s proteome, lipidome, and metabolome under fermentative and respiratory culture conditions, which correspond to the metabolic states of replicative and chronologically aging cells, respectively. Examination of the relationships among extended longevity phenotypes, protein, and metabolite levels revealed that although many of these nuclear-encoded mitochondrial genes carry out different functions, their inhibition attenuates a common mechanism that controls cytosolic ribosomal protein abundance, actin dynamics, and proteasome function to regulate lifespan. The principles of lifespan control learned through this work may be applicable to the regulation of lifespan in more complex organisms, since many aspects of mitochondrial function are highly conserved among eukaryotes.
Hunger signalling in the olfactory bulb primes exploration, food-seeking and peripheral metabolism
Authors: Stark Romana , Dempsey Harry , Kleeman Elizabeth , Sassi Martina , Davies Jeffrey , Zigman Jeffrey M. , Andrews Zane B. 
Published: January 2023

AbstractGrowing evidence highlights a complex interaction between olfaction and metabolism with impaired olfactory function observed in obesity and increased olfactory sensitivity during hunger. The mechanisms linking metabolic state and olfaction remain unknown, but increased accessibility of hormones, such as ghrelin, and the diverse expression of hormone receptors such as those for ghrelin (GHSRs) in the olfactory system suggests an underappreciated neuroendocrine role. Here, we examined the hypothesis that GHSRs in the olfactory bulb (OB) link hunger with olfactory sensitivity to influence foraging behaviours and metabolism. Selective deletion of OBGHSRsin adult male mice was achieved with adeno-associated viral expression of cre-recombinase in the OB of floxed-Ghsrmice. OBGHSRdeletion significantly affected olfactory discrimination and habituation to both food and pheromone odours, with greatest effect under fasted conditions. Anxiety-like and depression-like behaviour was significantly greater after OBGHSRdeletion using 3 independent anxiety behavioural tasks and testing for anhedonia, whereas exploratory behaviour was reduced. No effect on spatial navigation and memory was observed. Although OBGHSRdeletion did not affect cumulative food intake, it significantly impacted feeding behaviour as evidenced by altered bout number and duration. Moreover, food-finding after fasting or ip ghrelin was attenuated. Intriguingly, OBGHSRdeletion caused an increase in body weight and fat mass, spared fat utilisation on a chow diet and impaired glucose metabolism indicating metabolic dysfunction. We conclude that OBGHSRsmaintain olfactory sensitivity, particularly during hunger, and facilitate behavioural adaptations that optimise food-seeking in anxiogenic environments, priming metabolic pathways in preparation for food consumption.
Assessing the functional impact of protein binding site definition
Authors: Nandigrami Prithviraj , Fiser Andras 
Published: January 2023

ABSTRACTMany biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data this approach generates, it also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also provides guidance on the minimum expected accuracy of interface definition that is required to capture the biological function of a protein.AUTHOR SUMMARYThe residue level description or prediction of protein interfaces is a critical input for protein engineering and classification of function. However, different parametrizations of the same methods and especially alternative methods used to define the interface of a protein can return substantially different sets of residues. Typical experimental or computational methods employ mutational studies to verify interface definitions, but all these approaches inherently suffer from the problem that in order to probe the importance of any one position of an interface, an unknown amount of alteration is introduced into the very interface being studied. In this work, we employ a pharmacophore-based approach to computationally explore the consequences of defining alternative binding sites. The pharmacophore generates a hypothesis for the complementary protein binding interface, which then can be used in a search to identify the corresponding ligand from a library of candidates. The accurate ranking of cognate ligands can inform us about the biological accuracy of the interface definition. This study also provides a guideline about the minimum required accuracy of protein interface definitions that still provides a statistically significant recognition of cognate ligands above random expectation, which in turn sets a minimum expectation for interface prediction methods.
The Effect of Electrical Resonance in Neurons upon the Instability of Electrical Nerve Stimulations
Authors: Yu Shoujun , Yue Wenji , Guo Tianruo , Liu Yonghong , Zhang Yapeng , Khademi Sara , Zhou Tian , Xu Zhen , Song Bing , Wu Tianzhun , Liu Fenglin , Tai Yanlong , Yu Xuefei , Wang Hao 
Published: January 2023

AbstractRepetitive electrical nerve stimulation can induce a long-lasting perturbation of the axon’s membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanisms underlying stimulus-induced instability is still an open question due to the lack of a proper theoretical model. This study proposes a new method based on a Circuit-Probability theory to reveal the interlinkages between the electrical resonance of neurons and the instability of neural response. Supported by in vivo investigations, this new model predicts several key characteristics of stimulus-induced instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons.
Ensemble Approach for Predicting the Diagnosis of Osteoarthritis Using Soft Voting Classifier
Authors: Kim Jun-hee 
Published: January 2023

AbstractBackgroundOsteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and gender and modifiable factors such as physical activity.PurposeThis study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and to identify important variables in constructing the model through permutation importance.MethodUsing the RFECV technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining the RandomForest, XGBoost, and LightGBM algorithms was constructed, and the predictive performance and permutation importance of each variable were evaluated.ResultThe variables selected to construct the model were age, gender, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and gender (0.034).ConclusionThe performance of the model for predicting OA was relatively good, and if this model is continuously used and updated, this model could readily be used to predict OA diagnosis and the predictive performance of OA may be further improved.