bluuebunny/crossref_metadata_2025_split huggingface dataset:
Essential oil terpenes may inhibit steroidogenic cytochrome P450 activities
Authors: Sharma Katyayani , Lanzilotto Angelo , Yakubu Jibira , Therkelsen Søren , Vöegel Clarissa Daniela , Toit Therina Du , Jørgensen Flemming Steen , Pandey Amit V. 
Published: November 2023

AbstractEndocrine-disrupting chemicals (EDCs) may impact the development of Prostate Cancer (PCa) by altering the steroid metabolism. Although their exact mechanism of action in controlling tumor growth is not known, EDCs may inhibit steroidogenic enzymes such as Cytochrome P450 c17 (CYP17A1) or aromatase (CYP19A1) involved in the production of Androgens or Estrogens. High levels of circulating androgens are linked to PCa in men and Polycystic Ovary Syndrome (PCOS) in women. Essential Oils or their metabolites (EOs) like lavender oil and tea tree oil have been reported to act as potential EDCs and contribute towards sex steroid imbalance in case of prepubertal gynecomastia in boys and premature thelarche in girls due to the regular exposure to lavender-based fragrances among Hispanic population. We screened a range of EO components to determine their effects on CYP17A1 and CYP19A1 Computational docking was performed to predict the binding of EOs with CYP17A1 and CYP19A1 and functional assays were done using the radiolabeled substrates or Liquid Chromatography high-resolution Mass Spectrometry and cell viability assays were carried out in LNCaP cells. Many of the tested compounds bind close to the active site of CYP17A1, and (+)-Cedrol had the best binding with CYP17A1 and CYP19A1. Eucalyptol, Dihydro-β-Ionone & (-)-α-pinene showed 20% to 40% inhibition of dehydroepiandrosterone production; and some compounds also effected CYP19A1. Extensive use of these EOs in various beauty and hygiene products is common, but only a limited knowledge about their potential detrimental side effects exists. Our results suggest that prolonged exposure to some of these essential oils may result in steroid imbalances. On the other hand, due to their effect on lowering androgen output, ability to bind at the active site of steroidogenic cytochrome P450s, these compounds may provide design ideas for the novel compounds against hyperandrogenic disorders such as PCa and PCOS.
Structural determinants of pure antiestrogenicity
Authors: Diennet M. , Shaikh F. , El Ezzy M. , Thiombane K. , Weber S. , Poupart J. , Mendoza-Sanchez R. , Gao Z. , Gleason J. , Schuetz D.A. , Greene G. , Marinier A. , Fanning S.W. , Mader S 
Published: November 2023

AbstractInhibitory activities of antiestrogens on estrogen receptor alpha (ERα) range from mixed antagonism/agonism (selective ER modulators; SERMs) to complete antiestrogenicity associated with accelerated ERα turnover (selective ER degraders; SERDs). Here, we show using a panel of SERMs, SERDs and a PROTAC that efficient induction of ERα SUMOylation better accounts for complete transcriptional repression than increased suppression of coactivator recruitment or accelerated ERα degradation. Antiestrogen-induced ERα SUMOylation depends on the hydrophobicity of N-terminal residues of ligand binding domain (LBD) helix 12 (H12). L536 mutations, including those occurring in endocrine therapy-resistant breast cancer, abolished SUMOylation with all antiestrogens. Structures of the L536S human ERα LBD bound to fulvestrant analogs and molecular dynamics simulations predict dynamic side chain interactions with wild-type ERα H12 in the coactivator-binding groove and model the impact of L536 mutations. Together, our results show that induced SUMOylation contributes to the complete suppression of ERα activity and provide insights into its structural determinants.
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
Authors: Grobecker Pascal , van Nimwegen Erik 
Published: November 2023

AbstractSingle-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with ‘similar’ expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionalityreduction, employad hocmeasures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods.Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstateswhich operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters.We show that, on synthetic datasets, Cellstatesalmost perfectly recovers optimal partitions. On real data, Cellstatesrobustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstatesidentifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstatestool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important marker genes at each branch of this hierarchy, and to visualize these results.
Autophagy regulates tumor growth and metastasis
Authors: Qiang Lei , Zhao Baozhong , Ming Mei , Wang Ning , He Tong-Chuan , Hwang Seungmin , Thorburn Andrew , He Yu-Ying 
Published: November 2023

ABSTRACTThe role of autophagy in tumorigenesis and tumor metastasis remains poorly understood. Here we show that inhibition of autophagy stabilizes the transcription factor Twist1 through Sequestosome-1 (SQSTM1, also known as p62) and thus increases cell proliferation, migration, and epithelial-mesenchymal transition (EMT) in tumor development and metastasis. Inhibition of autophagy or p62 overexpression blocks Twist1 protein degradation in the proteasomes, while p62 inhibition enhances it. SQSTM1/p62 interacts with Twist1 via the UBA domain of p62, in a Twist1-ubiquitination-dependent manner. Lysine 175 in Twist1 is critical for Twist1 ubiquitination, degradation, and SQSTM1/p62 interaction. For squamous skin cancer and melanoma cells that express Twist1, SQSTM1/p62 increases tumor growth and metastasis in mice. Together, our results identified Twist1 as a key downstream protein for autophagy and suggest a critical role of the autophagy/p62/Twist1 axis in cancer development and metastasis.
Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer
Authors: Alsharoh Hasan 
Published: November 2023

AbstractPancreatic cancer (PC) is associated with high mortality overall. Recent literature has focused on investigating long noncoding RNAs (lncRNAs) in several cancers, but studies on their functions in PC are lacking. The purpose of this study was to identify novel lncRNAs and utilize machine learning to techniques to predict metastatic cases of PC using the identified lncRNAs. To identify significantly altered expression of lncRNA in PC, data was collected from The Cancer Genome Atlas (TCGA) and RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas were extracted for differential gene expression analysis. To assess the contribution of these lncRNAs to metastatic progression, different ML algorithms were used, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC) and eXtreme Gradient Boosting Classifier (XGBC). To improve the predictive accuracy of these models, hyperparameter tuning was performed, in addition to reducing bias through the synthetic minority oversampling technique. Out of 60,660 gene transcripts shared between 151 PC patients, 38 lncRNAs that were significantly differentially expressed were identified. To further investigate the functions of the novel lncRNAs, gene set enrichment analysis (GSEA) was performed on the population lncRNA panel. GSEA results revealed enrichment of several terms implicated in proliferation. Moreover, using the 4 ML algorithms to predict metastatic progression returned 76% accuracy for both SVM and RFC, explicitly based on the novel lncRNA panel. To the best of my knowledge, this is the first study of its kind to identify this lncRNA panel to differentiate between non-metastatic PC and metastatic PC, with many novel lncRNAs previously unmapped to PC. The ML accuracy score reveals important involvement of the detected RNAs. Based on these findings, I suggest further investigations of this lncRNA panelin vitroandin vivo, as they could be targeted for improved outcomes in PC patients, as well as assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors.
Transcranial cortex-wide imaging of murine ischemic perfusion with large-field multifocal illumination fluorescence microscopy
Authors: Chen Zhenyue , Zhou Quanyu , Droux Jeanne , Liu Yu-Hang , Glück Chaim , Gezginer Irmak , Wyss Matthias , Yoshihara Hikari AI , Kindler Diana Rita , Weber Bruno , Wegener Susanne , El Amki Mohamad , Razansky Daniel 
Published: November 2023

AbstractIschemic stroke is a common cause of death worldwide and a main cause of morbidity. Presently, computed tomography and magnetic resonance imaging are the mainstay for stroke diagnosis and therapeutic monitoring. These modalities are often limited in terms of accessibility as well as their ability to map brain perfusion with sufficient spatial and temporal resolution, particularly in the context of preclinical studies, thus calling for development of new brain perfusion techniques featuring rapid imaging speed, cost-effectiveness, and ease of use. Herein, we report on cortex-wide perfusion imaging in murine ischemic stroke with large-field multi-focal illumination fluorescence microscopy (LMI). We attained quantitative readings of hemodynamic and structural changes in cerebral vascular network and pial vessels at capillary level resolution and 80 Hz frame rate fully transcranially. Thein vivoperfusion maps accurately delineated the ischemic core and penumbra, further exhibiting strong correlation withex vivotriphenyl tetrazolium chloride staining. Interestingly, monitoring of therapeutic effects of thrombolysis in stroke has revealed that early recanalization could effectively save the penumbra whilst reducing the infarct area. Furthermore, cross-strain comparison of perfusion dynamics affirmed that C57BL/6 mice, benefiting from more extensive pial collateral recruitment, feature a larger penumbra and smaller infarct core as compared to BALB/c mice which have few or no collaterals. Finally, we apply LMI to show that sensory stimulation-based treatment enhances blood flow and abolish perfusion deficit in the ischemic core and penumbra regions. The simple, cost-effective and minimally invasive nature of the proposed approach offers new venues for brain perfusion research under various disease conditions such as stroke, neurodegeneration or epileptic seizures.
Phylogenomic insights into the first multicellular streptophyte
Authors: Bierenbroodspot Maaike J. , Darienko Tatyana , de Vries Sophie , Fürst-Jansen Janine M.R. , Buschmann Henrik , Pröschold Thomas , Irisarri Iker , de Vries Jan 
Published: November 2023

SUMMARYStreptophytes are best known as the clade containing the teeming diversity of embryophytes (land plants)1–4. Next to embryophytes are however a range of freshwater and terrestrial algae that bear important information on the emergence of key traits of land plants. Among these, the Klebsormidiophyceae stand out. Thriving in diverse environments—from mundane (ubiquitous occurrence on tree barks and rocks) to extreme (from the Atacama Desert to the Antarctic); Klebsormidiophyceae can exhibit filamentous body plans and display remarkable resilience as colonizers of terrestrial habitats5,6. Currently, the lack of a robust phylogenetic framework for the Klebsormidiophyceae hampers our understanding of the evolutionary history of these key traits. Here, we conducted a phylogenomic analysis utilizing advanced models that can counteract systematic biases. We sequenced 24 new transcriptomes of Klebsormidiophyceae and combined them with 14 previously published genomic and transcriptomic datasets. Using phylogenomic analysis built on 420 loci and sophisticated models, we establish a novel phylogenetic structure, dividing the six distinct genera of Klebsormidiophyceae in a novel four-order-system, with deep divergences more than 898, 765, and 734 million years ago. The reconstruction of ancestral states for habitat suggests an evolutionary history of multiple transitions between terrestrial-aquatic habitats, with Klebsormidiales having conquered land earlier than embryophytes. Focusing on the body plan of the last common ancestor of Klebsormidiophyceae, we postulate it was likely filamentous whereas the sarcinoids and unicells in Klebsormidiophyceae are likely derived states. Our data reveal that the first multicellular streptophytes likely lived more than 900 million years ago.
Computing Linkage Disequilibrium Aware Genome Embeddings using Autoencoders
Authors: Taş Gizem , Westerdijk Timo , Postma Eric ,   Project MinE ALS GWAS Consortium, Veldink Jan H. , Schönhuth Alexander , Balvert Marleen 
Published: November 2023

AbstractMotivationThe completion of the human genome has paved the way for genome-wide association studies (GWAS), which have already succeeded in explaining certain proportions of heritability. GWAS are not optimally suited to detect potential non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using e.g. deep neural networks are currently under active development. Despite their promise to scale to high-dimensional data, deep neural networks (DNNs) are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore calls for precaution in the form of dimensionality reduction.ResultsWe propose a method to compress genotyping data, involving single nucleotide polymorphisms (SNPs), while leveraging the LD structure in the genome and preserving non-linear relations among variants. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders that are able to learn a compressed representation of the block’s relevant genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and to bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE which involves a total of 23,209 ALS cases and 90,249 healthy controls. We compressed the haplotype blocks of an entire chromosome using our autoencoder-based approach, and show that this leads to more than 99% average test reconstruction accuracy - i.e. minimal information loss - while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform their linear alternative Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage genome haplotype structure and deep neural networks for dimensionality reduction of genetic data.Availability and ImplementationData used in this study are available for academic use through the Project MinE Consortium athttps://www.projectmine.com/research/data-sharing/. Data accessibility is contingent upon any terms or requirements specified by the source studies. Codes for data compression are available athttps://github.com/gizem-tas/haploblock-autoencoders.
The SNARC effect involves both representation and response-selection processing stages: Evidence from ERPs
Authors: Weibin Yang , Yan Lizhu , Xiang Xinrui , Nan Weizhi 
Published: November 2023

AbstractThe processing stage (i.e., the early semantic representation stage, the late response-selection stage, or both) at which the spatial-numerical association of response codes (SNARC) effect occurs is still controversial. The two-stage processing model hypothesizes that the SNARC effect involves both stages and that different interference factors acting at the two stages might be the core reason for the observed stage flexibility of the SNARC effect. To test this hypothesis, the present study was designed to elicit the SNARC, Stroop (semantic-representation stage related), and Simon (response-selection stage related) effects together in one magnitude comparison task and used the event-related potentials (ERPs) to observe the temporal dynamics of these effects. The behavioral results showed no interaction between the Stroop and Simon effects, while these two effects both interacted with the SNARC effect. Furthermore, the ERP results showed an interaction of the Stroop effect with the SNARC effect for the early sensory P1 component, while the interaction of the Simon effect with the SNARC effect was evident for the late N2 and P300 components. The current study repeatedly verified the independence of Stroop and Simon effects. Most importantly, the temporal-specific interactions among the SNARC effect and the other two stage-related factors provided further evidence to support the two-stage processing model that the SNARC effect involves both the representation and response-selection stages.
Extensive loss of forage diversity in social bees due to flower constancy and communication in simulated environments
Authors: Grüter Christoph , Segers Francisca , Hayes Lucy 
Published: November 2023

AbstractBees require a diverse diet for a healthy development. Many bee species show flower constancy, that is, they visit flowers of just one species during a foraging trip. Flower constancy is important for plant reproduction, but it could impair dietary diversity in bees, especially in biodiversity-depleted, human-modified landscapes. It is assumed that flower constancy does not lower dietary diversity in social bees, such as honey bees or bumble bees, because different colony members can specialise on different plant species. However, this has never been tested. We used computer simulations to investigate the effects of flower constancy on colony diet in plant species-rich and species-poor landscapes. We also explored if communication about food sources, which is used in many social bees, further reduces forage diversity. Our simulations reveal an extensive loss of forage diversity due to flower constancy in both plant species-rich and species-poor environments. Small colonies often discovered only 30-50% of all available plant species, thereby increasing the risk of nutritional deficiencies. Our simulation results could explain why bumble bees, which have small colony sizes, are less flower constant than honey bees and stingless bees, which have larger colony sizes. Remarkably, when colonies also communicated about food sources,Simpson’s diversity, which measures the evenness of flower visits, approached near zero in plant species-poor environments. Finally, we found that food source clustering, but not habitat fragmentation impaired dietary diversity. These findings can help in the design of landscapes that increase forage diversity and improve bee nutrition and health in human-modified landscapes.
Insomnia subtypes have differentiating deviations in brain structural connectivity
Authors: Bresser T. , Blanken T.F. , de Lange S. C. , Leerssen J. , Foster-Dingley J.C. , Lakbila-Kamal O. , Wassing R. , Ramautar J.R. , Stoffers D. , van de Heuvel M.P. , van Someren E.J.W. 
Published: November 2023

AbstractObjectiveInsomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure involved brain circuits. The present study investigated whether structural brain connectivity deviations differ between recently discovered and validated insomnia subtypes.MethodsStructural and diffusion weighted 3-Tesla MRI data of four independent studies were harmonized. The sample consisted of 73 controls without sleep complaints and 204 participants with insomnia grouped into five subtypes based on their fingerprint of personality and mood traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age, sex, and brain volume evaluated group differences in structural connectivity strength, indicated by fractional anisotropy and mean diffusivity, and evaluated within two different atlases.ResultsInsomnia subtypes showed differentiating profiles of deviating structural connectivity which moreover concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in four of the five subtypes:highly distressed(p=0.019), moderately distressed reward insensitive(p=0.014), slightly distressed low reactive(p=0.006) andslightly distressed high reactive(p=0.006).ConclusionsOur results provide a first indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping of insomnia could be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.
An explainable model using Graph-Wavelet for predicting biophysical properties of proteins and measuring mutational effects
Authors: Mishra Shreya , Pandey Neetesh , Rawat Atul , Srivastava Divyanshu , Ray Arjun , Kumar Vibhor 
Published: November 2023

ABSTRACTProteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding.