bluuebunny/crossref_metadata_2025_split huggingface dataset:
ABHD4 regulates adipocyte differentiation in vitro but does not affect adipose tissue lipid metabolism in mice
Authors: Seramur Mary , Sink Sandy , Cox Anderson O’Brien , Furdui Cristina M. , Key Chia-Chi Chuang 
Published: December 2022

AbstractAlpha/beta hydrolase domain-containing protein 4 (ABHD4) catalyzes the deacylation ofN-acyl phosphatidyl-ethanolamine (NAPE) and Lyso-NAPE to produce Glycerophospho-N-acyl ethanolamine (GP-NAE). Through a variety of metabolic enzymes, NAPE, Lyso-NAPE, and GP-NAE are ultimately converted into NAE, a group of bioactive lipids that control many physiological processes including inflammation, cognition, and food intake (i.e., oleoylethanolamide or OEA). In a diet-induced obese mouse model, adipose ABHD4 gene expression positively correlated with adiposity. However, it is unknown whether ABHD4 is a causal or a reactive gene to obesity. To fill this knowledge gap, we generated an ABHD4 knockout (KO) 3T3-L1 pre-adipocyte. During adipogenic stimulation, ABHD4 KO pre-adipocytes had increased adipogenesis and lipid accumulation, suggesting ABHD4 is responding to (a reactive gene), not contributing to (not a causal gene), adiposity and may serve as a mechanism for protecting against obesity. However, we did not observe any differences in adiposity and metabolic outcomes between adipocyte specific ABHD4 KO mice or whole body ABHD4 KO mice and their littermate control mice on chow or a high fat diet. This might be because we found that deletion of ABHD4 did not affect NAE such as OEA production, even though ABHD4 was highly expressed in adipose tissue and correlated with fasting adipose OEA levels and lipolysis. These data suggest that ABHD4 plays a role in adipocyte differentiation in vitro but not in adipose tissue lipid metabolism in mice despite nutrient overload, possibly due to compensation from other NAPE and NAE metabolic enzymes.
Tamoxifen exacerbates morbidity and mortality in mice receiving medetomidine anaesthesia
Authors: Rashbrook Victoria S. , Denti Laura , Ruhrberg Christiana 
Published: December 2022

AbstractTamoxifen-induced CreER-LoxP recombination is often used to induce spatiotemporally controlled gene deletion in genetically modified mice. Prior work has shown that tamoxifen and tamoxifen-induced CreER activation can have off-target effects that should be controlled. However, it has not yet been reported whether tamoxifen administration, independently of CreER expression, interacts adversely with commonly used anaesthetic drugs such as medetomidine or its enantiomer dexmedetomidine in mice. Here we report a high incidence of urinary plug formation and morbidity in male mice on a mixed C57Bl6/J and 129/SvEv background when tamoxifen treatment was followed by ketamine-medetomidine anaesthesia. Medetomidine is therefore contra-indicated for male mice after tamoxifen treatment. As dexmedetomidine causes morbidity and mortality in male mice at higher rates than medetomidine even without tamoxifen treatment, our findings suggest that dexmedetomidine is not a suitable alternative for anaesthesia of male mice after tamoxifen treatment. We conclude that the choice of anaesthetic drug needs to be carefully evaluated in studies using male mice that have undergone tamoxifen treatment for inducing CreER-LoxP recombination.
Patterns of secondary invasion in the understory of exotic, invasive timber stands
Authors: Jobin Varughese , Das Arundhati , Harikrishnan C.P. , Chanda Ritobroto , Lawrence Swapna , Robin V.V. 
Published: December 2022

AbstractCurrent climate and land cover change threaten global mountaintops with increased spread of invasive species. Long-established plantations of exotic and invasive trees on these mountaintops can alter their surroundings, further increasing invader-facilitated or secondary invasion. Identifying the ecological conditions that promote such specific associations can help develop better management interventions.The Western Ghats’s Shola Sky Islands (>1400m MSL) host vast stretches of exotic and invasive tree plantations that sustain colonisation of other invasive woody, herbaceous and fern species in their understories. Here we analysed vegetation and landscape variables from 232 systematically-placed plots in randomly selected grids using NMDS and Phi Coefficient approaches, to examine patterns of association (positive interactions) between secondary understory invasive species with specific exotic and invasive overstory species. We also conducted GLMM with zero inflation to determine the influence of environmental variables where such associations occur.We find that secondary invasion of multiple species under the canopy of other exotic invasives is widespread across the Shola Sky Islands. Stands of Eucalyptus host the colonisation of 70% of non-native invasive species surveyed across the Shola Sky Islands. In particular, Lantana camara invasion is strongly associated with Eucalyptus stands.We also found that climatic variables affect the colonisation of understorey woody invasive species, while invasion by exotic herbaceous species is associated with the density of road networks.. Canopy cover impacts all invasives negatively, while incidence of fire was negatively associated with invasion byLantana sppand thePteridium spp. While the restoration of natural habitats largely targets the highly invasive Acacia, less invasive Eucalyptus and Pine are often not included. Our study suggests that retaining such exotic species in natural habitats, particularly protected areas, can hinder ongoing restoration efforts by facilitating further invasions by multiple woody and herbaceous species.
Comparing Genomic and Epigenomic Features across Species Using the WashU Comparative Epigenome Browser
Authors: Zhuo Xiaoyu , Hsu Silas , Purushotham Deepak , Chen Samuel , Li Daofeng , Wang Ting 
Published: December 2022

AbstractGenome browsers have become an intuitive and critical tool to visualize and analyze genomic features and data. Conventional genome browsers display data/annotations on a single reference genome/assembly; there are also genomic alignment viewer/browsers that help users visualize alignment, mismatch, and rearrangement between syntenic regions. However, there is a growing need for a comparative epigenome browser that can display genomic and epigenomic datasets across different species and enable users to compare them between syntenic regions. Here, we present the WashU Comparative Epigenome Browser (http://comparativegateway.wustl.edu). It allows users to load functional genomic datasets/annotations mapped to different genomes and display them over syntenic regions simultaneously. The browser also displays genetic differences between the genomes from single nucleotide variants (SNVs) to structural variants (SVs) to visualize the association between epigenomic differences and genetic differences. Instead of anchoring all datasets to the reference genome coordinates, it creates independent coordinates of different genome assemblies to faithfully present features and data mapped to different genomes. It uses a simple, intuitive genome-align track to illustrate the syntenic relationship between different species. It extends the widely used WashU Epigenome Browser infrastructure and can be expanded to support multiple species. This new browser function will greatly facilitate comparative genomic/epigenomic research, as well as support the recent growing needs to directly compare and benchmark the T2T CHM13 assembly and other human genome assemblies.
High-throughput Approaches to Uncover Synergistic Drug Combinations in Leukemia
Authors: Chory Emma J. , Wang Meng , Ceribelli Michele , Michalowska Aleksandra M , Golas Stefan , Beck Erin , Klumpp-Thomas Carleen , Chen Lu , McKnight Crystal , Itkin Zina , Divakaran Sanjay , Bradner James , Khan Javed , Gryder Berkley E. , Thomas Craig J. , Stanton Benjamin Z. 
Published: December 2022

ABSTRACTWe report a comprehensive drug synergy study in acute myeloid leukemia (AML). In this work, we investigate 11 cell lines spanning both MLL-rearranged and non-rearranged subtypes. The work comprises a resource for the community, with many synergistic drug combinations that could not have been predicteda priori, and open source code for automation and analyses. We base our definitions of drug synergy on the Chou-Talalay method, which is useful for visualizations of synergy experiments in isobolograms, and median-effects plots, among other representations. Our key findings include drug synergies affecting the chromatin state, specifically in the context of regulation of the modification state of histone H3 lysine-27. We report open source high throughput methodology such that multidimensional drug screening can be accomplished with equipment that is accessible to most laboratories. This study will enable preclinical investigation of new drug combinations in a lethal blood cancer, with data analysis and automation workflows freely available to the community.Abstract Figure
KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description
Authors: Ma Chunyu , Zhou Zhihan , Liu Han , Koslicki David 
Published: December 2022

ABSTRACTBackgroundComputational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings.ResultsIn this work, we proposeKGML-xDTD: aKnowledgeGraph-basedMachineLearning framework for explainably predictingDrugsTreatingDiseases. It is a two-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable mechanisms of action (MOAs). We leverage knowledge-and-publication based information to extract biologically meaningful “demonstration paths” as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths.ConclusionsKGML-xDTDis the first model framework that can offer KG-path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce “black-box” concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations, and further accelerate the process of drug discovery for emerging diseases.
Battle of Polio eradication in the Western Pacific Region in the transition to COVID-19 endemicity
Authors: Mei Ruobing , Kwok Shirley L. L. , Lau Eric H. Y. , Lo Tiffany H. K. , Wu Joseph T. , Lin Leesa K. , Leung Kathy 
Published: December 2022

AbstractThe Polio eradication campaign has been set back substantially since 2020 due to the COVID-19 pandemic. Recent detections of poliovirus transmission in multiple high-income countries suggest suboptimal population immunity in many parts of the world even though polio vaccination has been included in routine childhood immunization for decades. We reviewed polio vaccination schedules and vaccine uptake in the Western Pacific Region countries and assessed the potential shortfall in population immunity against polio resurgence across these populations. In addition, we conducted a repeated cross-sectional study between 2021 and 2022 in the Western Pacific Region to understand factors contributing to polio vaccine hesitancy. Our results reveal potential shortfalls in population immunity against polio in Western Pacific Region and provide insights into how vaccination programs and campaigns can be strengthened to ensure continual progress towards polio eradication.
GOThresher: a program to remove annotation biases from protein function annotation datasets
Authors: Joshi Parnal , Banerjee Sagnik , Hu Xiao , Khade Pranav M. , Friedberg Iddo 
Published: December 2022

AbstractMotivationAdvances in sequencing technologies have led to a surge in genomic data, although the functions of many gene products coded by these genes remain unknown. While in-depth, targeted experiments that determine the functions of these gene products are crucial and routinely performed, they fail to keep up with the inflow of novel genomic data. In an attempt to address this gap, high-throughput experiments are being conducted in which a large number of genes are investigated in a single study. The annotations generated as a result of these experiments are generally biased towards a small subset of less informative Gene Ontology (GO) terms. Identifying and removing biases from protein function annotation databases is important since biases impact our understanding of protein function by providing a poor picture of the annotation landscape. Additionally, as machine learning methods for predicting protein function are becoming increasingly prevalent, it is essential that they are trained on unbiased datasets. Therefore, it is not only crucial to be aware of biases, but also to judiciously remove them from annotation datasets.ResultsWe introduce GOThresher, a Python tool that identifies and removes biases in function annotations from protein function annotation databases.Implementation and AvailabilityGOThresher is written in Python and released via PyPIhttps://pypi.org/project/gothresher/and on the Bioconda Anaconda channelhttps://anaconda.org/bioconda/gothresher. The source code is hosted on GitHubhttps://github.com/FriedbergLab/GOThresherand distributed under the GPL 3.0 license.Contact{idoerg@iastate.edu|parnal@iastate.edu}
Convergent remodeling of the gut microbiome is associated with host energetic condition over long-distance migration
Authors: Trevelline Brian K. , Sprockett Daniel , DeLuca William V. , Andreadis Catherine R. , Moeller Andrew H. , Tonra Christopher 
Published: December 2022

ABSTRACTThe gut microbiome can be thought of as a ‘forgotten organ’, owing to its profound effects on host phenotypes. Long-distance migratory birds are capable of adaptively modulating their physiology, raising the hypothesis that the microbiome of migratory birds may undergo a parallel remodeling process that helps to meet the energetic demands of long-distance migration. To test this hypothesis, we investigated changes in gut microbiome composition and function over the fall migration of a Neotropical-Nearctic migratory Blackpoll Warbler (Setophaga striata), which exhibits one of the longest known autumnal migratory routes of any songbird and rapidly undergoes extensive physiological remodeling during migration. Overall, our results showed that the Blackpoll warbler microbiome differed significantly across phases of fall migration. This pattern was driven by a dramatic increase in the relative abundance of Proteobacteria, and more specifically a single ASV belonging to the family Enterobacteriaceae. Further, blackpolls exhibited a progressive reduction in microbiome phylogenetic diversity and within-group variances over migration, indicating convergence of microbiome composition among individuals during long-distance migration. Metagenomic analysis revealed that the gut microbiome of staging blackpolls was enriched in bacterial pathways involved in vitamin, amino acid, and fatty acid biosynthesis, as well as carbohydrate metabolism, and that these pathways were in turn positively associated with host body mass and subcutaneous fat deposits. Together, these results provide evidence that the gut microbiome of migratory birds may undergo adaptive remodeling to meet the physiological and energetic demands of long-distance migration.
Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets
Authors: Cho Hyunghoon , Froelicher David , Chen Jeffrey , Edupalli Manaswitha , Pyrgelis Apostolos , Troncoso-Pastoriza Juan R. , Hubaux Jean-Pierre , Berger Bonnie 
Published: December 2022

ABSTRACTSharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variants linked to health and disease1, 2. However, existing data sharing regulations limit the scope of such collaborations3. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or support only simplified analyses4–7. We introduce secure federated genome-wide association studies (SF-GWAS), a novel combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports the most widely-used GWAS pipelines based on principal component analysis (PCA) or linear mixed models (LMMs). We demonstrate the accuracy and practical runtimes of SF-GWAS on five datasets, including a large UK Biobank cohort of 410K individuals, showcasing an order-of-magnitude improvement in runtime compared to previous work. Our work realizes the power of secure collaborative genomic studies at unprecedented scale.
Plasmonic Stimulation of Gold Nanorods for the Photothermal Control of Engineered Living Materials
Authors: Basaran Selim , Dey Sourik , Bhusari Shardul , Sankaran Shrikrishnan , Kraus Tobias 
Published: December 2022

AbstractEngineered living materials (ELMs) use encapsulated microorganisms within polymeric matrices for biosensing, drug delivery, capturing viruses, and bioremediation. It is often desirable to control their function remotely and in real time. Suitable, genetically engineered microorganisms respond to changes of their environment. Here, we combine this local sensitivity with a nanostructured encapsulation material to sensitize the ELM for infrared light. Previously, blue light has been used to stimulate microorganisms that contain optogenetic modules responsive to those wavelengths without the need for exogenous cofactors. Here, we use plasmonic gold nanorods (AuNR) that have a strong absorption maximum at 808 nm, a wavelength where human tissue is relatively transparent. Biocompatible composites of a Pluronic-based hydrogel and AuNR are prepared without agglomeration; they react to illumination by local heating. We measure a photothermal conversion efficiency of 47 % in transient temperature measurements. Steady-state temperature profiles from local photothermal heating are quantified using infrared photothermal imaging, correlated with measurements inside the gel, and applied to stimulate thermoresponsive bacteria. Using a bilayer ELM construct with the thermoresponsive bacteria and the thermoplasmonic composite gel in two separate but connected hydrogel layers, it is shown that the bacteria can be stimulated to produce a fluorescent protein using infrared light in a spatially controlled manner.
Kinetic co-evolutionary models predict the temporal emergence of HIV resistance mutations under drug selection pressure
Authors: Biswas Avik , Choudhuri Indrani , Arnold Eddy , Lyumkis Dmitry , Haldane Allan , Levy Ronald M. 
Published: December 2022

AbstractDrug resistance in human immunodeficiency virus (HIV) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interactions between a DRM and other residues in HIV protein sequences, is found to be key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naïve patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, reverse transcriptase, and integrase, the primary targets of antiretroviral therapy (ART). The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV and other infectious agents.SignificanceHIV affects the lives of millions of patients worldwide; cases of pan-resistant HIV are emerging. We use kinetic Monte-Carlo methods to simulate the evolution of drug resistance based on HIV patient-derived sequence data available on public databases. Our simulations capture the timeline for the evolution of DRMs reported in the literature across the major drug-target enzymes – PR, RT, and IN. The network of epistatic interactions with the primary DRMs determines the rate at which DRMs are acquired. The timeline is not explained by the overall fitness of the DRMs or features of the genetic code. This work provides a framework for the development of computational methods that forecast the time course over which drug resistance to antivirals develops in patients.