bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative Proteomics Identifies Conserved Proteins and Altered Regulation of Mucin-16 in Low Grade Serous Ovarian Cancers
Authors: Tarney Christopher M. , Mhawech-Fauceglia Paulette , Ogata Jonathan D. , Oliver Julie , Abulez Tamara , Branton Philip A. , Movahedi-Lankarani Saeid , Hood Brian L. , Conrads Kelly A. , Rosalik Kendal , Wong Kwong-Kwok , Gershenson David M. , Lee Sanghoon , Sood Anil K. , Bast Robert C. , Darcy Kathleen M. , Phippen Neil T. , Maxwell G. Larry , Conrads Thomas P. , Bateman Nicholas W. 
Published: May 2024

AbstractBackgroundLow-grade serous ovarian carcinoma (LGSOC) is a rare and largely chemoresistant subtype of epithelial ovarian cancer. Unlike treatment for high-grade serous ovarian cancer (HGSOC), management options for LGSOC patients are limited, in part, due to a lack of deep molecular characterization of this disease. To address this limitation, we aimed to define highly conserved proteome alterations in LGSOC by performing deep quantitative proteomic analysis of tumors collected from LGSOC (n=12) and HGSOC (n=24) patients or normal fallopian tube tissues (n=12) and validating proteins within two independent proteomic datasets of LGSOC (n=25) and HGSOC (n=49) tumors.ResultsOur efforts identified 275 protein alterations conserved between LGSOC and HGSOC tumors that exhibit high quantitative correlation between discovery and validation cohorts (Spearman Rho ≥ 0.82, P < 1E-4). Conserved proteins elevated in LGSOC tumors were enriched for pathways regulating cell adhesion and defective cellular apoptosis signaling and candidates mapping as putative drug targets included 5’-nucleotidase/ cluster of differentiation 73 (NT5E/CD73). We also identified MUC16 (CA125) as significantly elevated in LGSOC versus HGSOC tumors and confirmed this by immunohistochemistry analysis between three independent reviewers. We also find that MUC16 exhibits a more apical versus membrane-staining pattern in LGSOC tumors, suggesting unique regulation of MUC16 in this disease subtype.ConclusionOur efforts define highly conserved protein alterations distinguishing LGSOC from HGSOC tumors, including CD73, as well as the novel identification that MUC16 is elevated and exhibits more apical staining pattern in LGSOC tumor tissues. These findings deepen our molecular understanding of LGSOC and provide unique insights into highly conserved proteome alterations in LGSOC tumors.
Spleen gene expression is associated with mercury content in three-spined stickleback populations
Authors: Yadav Brijesh S. , Lamaze Fabien C. , Shankregowda Aruna M. , Delahaut Vyshal , Calboli Federico C. F. , Patel Deepti M. , Kuizenga Marijn , Bervoets Lieven , Volckaert Filip A.M. , Boeck Gudrun De , Raeymaekers Joost A.M. 
Published: May 2024

AbstractMercury can be very toxic at low environmental concentrations by impairing immunological, neurological, and other vital pathways in humans and animals. Aquatic ecosystems are heavily impacted by mercury pollution, with evidence of biomagnification through the food web. We examined the effect of mercury toxicity on the spleen, one of the primary immune organs in fish, in natural populations of the three-spined stickleback (Gasterosteus aculeatusLinnaeus, 1758). Our aim was to better understand adaptation to high mercury environments by investigating transcriptomic changes in the spleen. Three stickleback populations with mean Hg muscle concentrations above and three populations with mean Hg muscle concentrations below the European Biota Quality Standard of 20 ng/g wet weight were selected from the Scheldt and Meuse basin in Belgium. We then conducted RNA sequencing of the spleen tissue of 22 females from these populations. We identified 136 differentially expressed genes between individuals from populations with high and low mean mercury content. The 129 genes that were upregulated were related to the neurological system, immunological activity, hormonal regulation, and inorganic cation transporter activity. Seven genes were downregulated and were all involved in pre-mRNA splicing. The results are indicative of our ability to detect molecular alterations in natural populations that exceed an important environmental quality standard. This allows us to assess the biological relevance of such standards, offering an opportunity to better describe and manage mercury-associated environmental health risks in aquatic populations.
NPmatch: Latent Batch Effects Correction of Omics data by Nearest-Pair Matching
Authors: Zito Antonino , Martinelli Axel , Masiero Mauro , Akhmedov Murat , Kwee Ivo 
Published: May 2024

AbstractMotivationBatch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPmatch, a nearest-neighbor matching-based method that adjusts BEs satisfactorily and outperforms current methods in a wide range of datasets.ResultsWe assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPmatch demonstrates overall superior performance in correcting for BEs while preserving biological differences than existing methods. Altogether, our method proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant.Data availability and implementationNPmatch is freely available on Github (https://github.com/bigomics/NPmatch) and on Omics Playground (https://bigomics.ch/omics-playground). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus (GEO) repository.
Distraction Impact of Concurrent Conversation on Event-Related Potential Based Brain-Computer Interfaces
Authors: Kim Minju , Kim Sung-Phil 
Published: May 2024

AbstractObjectiveThis study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.ApproachThe experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main resultsThe findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificanceThis study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.
Transcriptome Analysis ofAegle marmelos(L.) Corrêa Fruit and Leaf Tissues: An Underexploited Fruit Tree
Authors: Kaushik Prashant 
Published: May 2024

AbstractAegle marmelos(L) Correa is both a horticultural and medicinally important tree in South-East Asia. Both of its fruits and leaves have several medicinal values including treatment of certain chronic diseases. Despite its importance, the genomic resource of this tree is very scarce. The objectives of this work were to conduct a de novo transcriptome assembly of leaf tissue from A.marmelos, locally known as bael in India. Comparison studies were conducted between the fruit transcriptome and leaf transcriptome ofA. marmelos. This is the first time that the fruit transcriptomeof A. marmeloshas been reported in the literature. There were 47 million clean reads were generated for fruit tissue and 34 million clean reads of leaf tissue. The reads were assembled to 61, 860 unigenes. Around 83% of fruit transcripts were mapped to the original assembly whereas 89% of leaf transcripts were mapped to the original assembly. Whereas, in terms of molecular function, the gene ontology (GO) terms were related to binding, cellular activity and transporter activity and genes expressed only in leaf and fruit tissue were 14,578 and 11086, respectively, Overall, RNA sequencing may be an effective method for determining the pathway and genes in the underexploited fruit crops.
Decreased brain pH correlated with progression of Alzheimer’s disease neuropathology: a systematic review and meta-analyses of postmortem studies
Authors: Hagihara Hideo , Miyakawa Tsuyoshi 
Published: May 2024

AbstractBackgroundAltered brain energy metabolism is implicated in Alzheimer’s disease (AD). Limited and conflicting studies on brain pH changes, indicative of metabolic alterations associated with neural activity, warrant a comprehensive investigation into their relevance in this neurodegenerative condition. Furthermore, the relationship between these pH changes and established AD neuropathological evaluations, such as Braak staging, remains unexplored.MethodsWe conducted quantitative meta-analyses on postmortem brain and cerebrospinal fluid pH in patients with AD and non-AD controls, using publicly available demographic data. We collected raw pH data from studies in the NCBI GEO, PubMed, and Google Scholar databases.ResultsOur analysis of 17 datasets (457 patients and 315 controls) using a random-effects model showed a significant decrease in brain and cerebrospinal fluid pH in patients compared to controls (Hedges’g= –0.54,p< 0.0001). This decrease remained significant after considering postmortem interval, age at death, and sex. Notably, pH levels were negatively correlated with Braak stage, indicated by the random-effects model of correlation coefficients from 15 datasets (292 patients and 159 controls) (adjustedr= –0.26,p< 0.0001). Furthermore, brain pH enhanced the discriminative power of theAPOEε4 allele, the most prevalent risk gene for AD, in distinguishing patients from controls in a meta-analysis of four combined datasets (95 patients and 87 controls).ConclusionsThe significant decrease in brain pH in AD underlines its potential role in disease progression and diagnosis. This decrease, potentially reflecting neural hyperexcitation, could enhance our understanding of neurodegenerative pathology and aid in developing diagnostic strategies.
Application of graph models to the identification of transcriptomic oncometabolic pathways in human hepatocellular carcinoma
Authors: Barace Sergio , Santamaría Eva , Infante Stefany , Arcelus Sara , De la Fuente Jesús , Goñi Enrique , Tamayo Ibon , Ochoa Idoia , Sogbe Miguel , Sangro Bruno , Hernaez Mikel , Ávila Matías A. , Argemi Josepmaria 
Published: May 2024

AbstractWhole tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly coexpressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph models. Using The Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-Glycan, Mevalonate and Sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of Acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.
Development and IND-enabling studies of a novel Cas9 genome-edited autologous CD34+cell therapy to induce fetal hemoglobin for sickle cell disease
Authors: Katta Varun , O’Keefe Kiera , Li Yichao , Mayurathan Thiyagaraj , Lazzarotto Cicera R. , Wood Rachael K. , Levine Rachel M. , Powers Alicia , Mayberry Kalin , Manquen Garret , Yao Yu , Zhang Jingjing , Jang Yoonjeong , Nimmagadda Nikitha , Dempsey Erin A. , Lee GaHyun , Uchida Naoya , Cheng Yong , Fazio Frank , Lockey Tim , Meagher Mike , Sharma Akshay , Tisdale John F. , Zhou Sheng , Yen Jonathan S. , Weiss Mitchell J. , Tsai Shengdar Q. 
Published: May 2024

AbstractSickle cell disease (SCD) is a common severe blood disorder, caused by one major point mutation in theHBBgene. Current pharmacotherapies are only partially effective and potentially curative allogeneic hematopoietic stem cell transplantation (HSCT) is associated with immune toxicities. Genome editing of autologous patient hematopoietic stem cells (HSCs) to reactivate fetal hemoglobin (HbF) in erythroid progeny offers a potentially curative approach to treat SCD and circumvents some problems associated with allogeneic HSCT. Although the FDA has released guidelines for evaluating genome editing risks, it remains unclear how to best to assess the preclinical safety and efficacy of genome-edited cellular drug products to prepare for a clinical trial. Here we describe rigorous pre-clinical characterization and optimization of a therapeutic γ-globin gene promoter editing strategy that supported an investigational new drug (IND) application cleared by the FDA. We compared targets in the γ-globin promoter andBCL11Aerythroid-specific enhancer, identified a lead candidate that potently induces HbF, and tested our approach in mobilized CD34+HSPCs from normal donors and individuals with SCD. We observed efficient editing, induction of HbF to levels predicted to be therapeutic, and reduction of sickling in red blood cells derived from edited HSPCs. With single-cell western and RNA-seq analyses, we defined the heterogeneity and specificity of HbF induction andHBG1/HBG2transcription. With CHANGE-seq for sensitive and unbiased genome-wide off-target discovery followed by multiplexed targeted sequencing, we did not detect off-target activity in edited HSPCs. Our study provides a blueprint for translating new discoveries onex vivogenome editing of HSCs towards clinical trials for treating SCD and other blood disorders.
Quantitative fluorescent nanoparticle tracking analysis and nano-flow cytometry enable advanced characterization of single extracellular vesicles
Authors: Mladenović Danilo , Brealey Joseph , Peacock Ben , Zarovni Nataša 
Published: May 2024

ABSTRACTCurrent state-of-the-art tools for analyzing extracellular vesicles (EVs) offer either highly sensitive but unidimensional bulk measurements of EV components, or high-resolution multiparametric single particle analyses which lack standardization and appropriate reference materials. This limits the accuracy of assessment of marker abundance and overall marker distribution among individual EVs, and finally, the understanding of true EV heterogeneity.In this study, we aimed to define the standardized operating procedures and reference material for fluorescent characterization of EVs with two commonly used EV analytical platforms - nanoparticle tracking analysis (NTA) and nano-flow cytometry (nFCM).For the first time, we achieved quantitative fluorescence analyses on ZetaView NTA and NanoAnalyzer nFCM instruments, by utilizing yellow-green FluoSpheres (FS) with assigned ERF (equivalent reference fluorophore) values. This standardization technique allowed for fluorescent EV signal to be expressed in ERF units (indicative of bound fluorescent antibodies per EV), thus enabling measurement of target protein marker abundance on individual EVs, and in the whole EV population. The NTA’s and nFCM’s limits of quantification (LoQ) were evaluated at 115 and 75 Alexa Fluor 488 (AF488) molecules, respectively. To complement these shortcomings, in-line bulk fluorescence measurements in a plate reader were performed. This provided absolute marker quantification, and more insightful analyses of EV heterogeneity and marker stoichiometry.The standardization method outlined in this work unlocks the full analytical potential of NTA and nFCM, enabling cross-platform data comparison. At the same time, it highlights some of the technical challenges and considerations, and thus contributes to the ongoing efforts towards development of EV analytical tools.
Mathematical Methodology for Dynamic Models of Insecticide Selection Assuming a Polygenic Basis of Resistance
Authors: Hobbs Neil Philip , Hastings Ian Michael 
Published: May 2024

AbstractMathematical models for evaluating insecticide resistance management (IRM) have primarily assumed insecticide resistance (IR) is monogenic. Modelling using a quantitative genetics framework to model polygenic IR has less frequently been used. We introduce a complex mathematical model for polygenic IR with a focus on public health insecticide deployments for vector control. Conventional polygenic models assume selection differentials are constant over the course of selection. We instead propose calculating the selection differentials dynamically depending on the level of IR and the amount of insecticide encountered. Dynamically calculating the selection differentials increases biological and operational realism, allowing for the evaluation of strategies of policy relevance, including reduced dose mixtures or the deployment of long-lasting insecticide-treated nets and indoor residual spraying in combination. The dynamic calculations of insecticide selection allow for two methods: 1) Truncation (“polytruncate”) – where only the most resistant individuals in the population survive, and 2) Probabilistic (“polysmooth”) – where an individual’s survival probability is dependent on their own level of IR. We describe in detail the calculation and calibration of these models. The models (“polytruncate” and “polysmooth”) are compared against a previous polygenic model (“polyres”) and the monogenic literature for the IRM strategies of rotations, sequences and full-dose mixtures. We demonstrate consistency in results of full-dose mixtures remaining the best IRM strategy, with sequences and rotations being similar in their efficacy between the two selection processes, and consistency in “global conclusions” with previous models. Consistency between the “polysmooth”, “polytruncate” and previous models helps provide confidence in their predictions, as operational interpretations are not overly impacted by model assumptions. This increases confidence in the application of these dynamic models to investigate more complex IRM strategies and scenarios, and their future applications will investigate more scenario specific evaluations of IRM strategies.
Mechano-metabolism of metastatic breast cancer cells in 2D and 3D microenvironments
Authors: Jaganathan Anuja , Toth Joshua , Chen Xingyu , Basir Ramin , Pieuchot Laurent , Shen Yihui , Reinhart-King Cynthia , Shenoy Vivek B. 
Published: May 2024

AbstractCells regulate their shape and metabolic activity in response to the mechano-chemical properties of their microenvironment. To elucidate the impact of matrix stiffness and ligand density on the bioenergetics of mesenchymal cells, we developed a nonequilibrium, active chemo-mechanical model that accounts for the mechanical energy of the cell and matrix, chemical energy from ATP hydrolysis, interfacial energy, and mechano-sensitive regulation of stress fiber assembly through signaling. By integrating the kinetics and energetics of these processes, we define the cell “metabolic potential” that, when minimized, provides testable predictions of cell contractility, shape, and ATP consumption. Specifically, we show that the morphology of MDA-MB-231 breast cancer cells in 3D collagen changes from spherical to elongated to spherical with increasing matrix stiffness, which is consistent with experimental observations. On 2D hydrogels, our model predicts a hemispherical-to-spindle-to-disc shape transition with increasing gel stiffness. In both cases, we show that these shape transitions emerge from competition between the energy of ATP hydrolysis associated with increased contractility that drives cell elongation and the interfacial energy that favors a rounded shape. Furthermore, our model can predict how increased energy demand in stiffer microenvironments is met by AMPK activation, which is confirmed experimentally in both 2D and 3D microenvironments and found to correlate with the upregulation of mitochondrial potential, glucose uptake, and ATP levels, as well as provide estimates of changes in intracellular adenosine nucleotide concentrations with changing environmental stiffness. Overall, we present a framework for relating adherent cell energy levels and contractility through biochemical regulation of underlying physical processes.Statement of SignificanceIncreasing evidence indicates that cellular metabolism is regulated by mechanical cues from the extracellular environment. Forces transmitted from the microenvironment activate mechanotransduction pathways in the cell, which trigger a cascade of biochemical events that impact cytoskeletal tension, cellular morphology and energy budget available to the cell. Using a nonequilibrium free energy-based theory, we can predict the ATP consumption, contractility, and shape of mesenchymal cancer cells, as well as how cells regulate energy levels dependent on the mechanosensitive metabolic regulator AMPK. The insights from our model can be used to understand the mechanosensitive regulation of metabolism during metastasis and tumor progression, during which cells experience dynamic changes in their microenvironment and metabolic state.