bluuebunny/crossref_metadata_2025_split huggingface dataset:
A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data
Authors: Madrigal Ariel , Lu Tianyuan , Soto Larisa M. , Najafabadi Hamed S. 
Published: August 2023

AbstractAnalysis of single cells across multiple samples and/or conditions encompasses a series of interrelated tasks, which range from normalization and inter-sample harmonization to identification of cell state shifts associated with experimental conditions. Other downstream analyses are further needed to annotate cell states, extract pathway-level activity metrics, and/or nominate gene regulatory drivers of cell-to-cell variability or cell state shifts. Existing methods address these analytical requirements sequentially, lacking a cohesive framework to unify them. Moreover, these analyses are currently confined to specific modalities where the biological quantity of interest gives rise to a singular measurement. However, other modalities require joint consideration of dual measurements; for example, modeling the latent space of alternative splicing involves joint analysis of exon inclusion and exclusion reads. Here, we introduce a generative model, called GEDI, to identify latent space variations in multi-sample, multi-condition single cell datasets and attribute them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with the state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states in the form of transcriptomic vector fields, and machine learning-based prediction of sample characteristics from single-cell data. By incorporating gene-level prior knowledge, it can further project pathway and regulatory network activities onto the cellular state space, enabling the computation of the gradient fields of transcription factor activities and their association with the transcriptomic vector fields of sample covariates. Finally, we demonstrate that GEDI surpasses the gene-centric approach by extending all these concepts to the study of alternative cassette exon splicing and mRNA stability landscapes in single cells.
Origin and modern microbial ecology of secondary mineral deposits in Lehman Caves, Great Basin National Park, NV, USA
Authors: Havlena Zoë E. , Hose Louise D. , DuChene Harvey R. , Baker Gretchen M. , Powell Doug , Labrado Amanda L. , Brunner Benjamin , Jones Daniel S. 
Published: August 2023

AbstractLehman Caves is an extensively decorated, high desert cave that represents one of the main tourist attractions in Great Basin National Park, Nevada. Although traditionally considered a water table cave, recent studies identified abundant speleogenetic features consistent with a hypogenic and, potentially, sulfuric acid origin. Here, we characterized white mineral deposits in the Gypsum Annex (GA) passage to determine whether these secondary deposits represent biogenic minerals formed during sulfuric acid corrosion, and explored microbial communities associated with these and other mineral deposits throughout the cave. Powder X-ray diffraction (pXRD), scanning electron microscopy with electron dispersive spectroscopy (SEM-EDS), and electron microprobe analyses (EPMA) showed that, while most white mineral deposits from the GA contain gypsum, they also contain abundant calcite, silica, and other phases. Gypsum and carbonate-associated sulfate isotopic values of these deposits are variable, with δ34SV-CDTbetween +9.7‰ and +26.1‰, and do not reflect depleted values typically associated with replacement gypsum formed during sulfuric acid speleogenesis. Petrographic observations show that the sulfates likely co-precipitated with carbonate and SiO2phases. Taken together, these data suggest that the deposits resulted from later stage meteoric events and not during an initial episode of sulfuric acid speleogenesis. Most sedimentary and mineral deposits in Lehman Caves have very low microbial biomass, with the exception of select areas along the main tour route that have been impacted by tourist traffic. High-throughput 16S rRNA gene amplicon sequencing showed that microbial communities in GA sediments are distinct from those in other parts of the cave. The microbial communities that inhabit these oligotrophic secondary mineral deposits include OTUs related to known ammonia-oxidizingNitrosococcalesand Thaumarchaeota, as well as common soil taxa such as Acidobacteriota and Proteobacteria. This study reveals microbial and mineralogical diversity in a previously understudied cave and expands our understanding of the geomicrobiology of arid, hypogene cave systems.
Tafazzin Mediates Tamoxifen Resistance by Regulating Cellular Phospholipid Composition in ER-Positive Breast Cancer
Authors: Li Xuan , Zhang Yuan , Zhang Tengjiang , Zhao Luyang , Lin Christopher G. , Hu Haitian , Zheng Hanqiu 
Published: August 2023

AbstractTamoxifen is the frontline therapeutic agent for the estrogen receptor-positive (ER+) subtype of breast cancer patients, which accounts for 70-80% of total breast cancer incidents. However, clinical resistance to tamoxifen has become increasingly common, highlighting the need to identify the underlying cellular mechanisms. In our study, we employed a genome-scale CRISPR-Cas9 loss-of-function screen and validation experiments to discover that Tafazzin (TAZ), a mitochondrial transacylase, is crucial for maintaining the cellular sensitivity of ER+ breast cancer cells to tamoxifen and other chemotherapies. Mechanistically, we found that cardiolipin, whose synthesis and maturation rely on TAZ, is required to maintain cellular resistance to tamoxifen. Loss of metabolic enzymatic activity of TAZ causes ERα downregulation and therapy resistance. Interestingly, we observed that TAZ deficiency also led to the upregulation of lysophosphatidylcholine (LPC), which in turn suppressed ERα expression and nuclear localization, thereby contributing to tamoxifen resistance. LPC is further metabolized to lysophosphatidic acid (LPA), a bioactive molecule that supports cell survival. Thus, our findings suggest that the depletion of TAZ promotes tamoxifen resistance through an LPC-LPA phospholipid synthesis axis, and targeting this lipid metabolic pathway could restore cell susceptibility to tamoxifen treatment.
Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition
Authors: Burtscher Mira L , Gade Stephan , Garrido-Rodriguez Martin , Rutkowska Ania , Werner Thilo , Eberl H Christian , Petretich Massimo , Knopf Natascha , Zirngibl Katharina , Grandi Paola , Bergamini Giovanna , Bantscheff Marcus , Fälth-Savitski Maria , Saez-Rodriguez Julio 
Published: August 2023

AbstractThe deregulation of complex diseases often spans multiple molecular processes. A multimodal functional characterization of these processes can shed light on the disease mechanisms and the effect of drugs. Thermal Proteome Profiling (TPP) is a mass-spectrometry based technique assessing changes in thermal protein stability that can serve as proxies of functional changes of the proteome. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a framework for causal integration of multi-omics, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover known mechanistic consequences of PARP inhibition in ovarian cancer cells on cell cycle and DNA damage response in detail and to uncover new insights into drug response mechanisms related to interferon and hippo signaling. We found that TPP complements the other omics data and allowed us to obtain a network model with higher coverage of the main underlying mechanisms. These results illustrate the added value of TPP, and more generally the power of network models to integrate the information provided by different omics technologies. We anticipate that this strategy can be used to broadly integrate functional proteomics with other omics to study complex molecular processes.Graphical abstract
Non-uniform contextual interactions in the visual cortex place fundamental limits on spatial vision
Authors: Morton Mitchell P. , Denagamage Sachira , Hudson Nyomi V. , Nandy Anirvan S. 
Published: August 2023

ABSTRACTA prevailing assumption in our understanding of how neurons in the primary visual cortex (V1) integrate contextual information is that such processes are spatially uniform. Conversely, perceptual phenomena such as visual crowding, the impaired ability to accurately recognize a target stimulus among distractors, suggest that interactions among stimuli are distinctly non-uniform. Prior studies have shown flankers at specific spatial geometries exert differential effects on target perception. To resolve this discrepancy, we investigated how flanker geometry impacted the representation of a target stimulus in the laminar microcircuits of V1. Our study reveals flanker location differentially impairs stimulus representation in excitatory neurons in the superficial and input layers of V1 by tuned suppression and untuned facilitation of orientation responses. Mechanistically, this effect can be explained by asymmetrical spatial kernels in a normalization model of cortical activity. Strikingly, these non-uniform modulations of neural representation mirror perceptual anisotropies. These results establish the non-uniform spatial integration of information in the earliest stages of cortical processing as a fundamental limitation of spatial vision.
Predicting human microbe-drug associations via graph attention network with multiple kernel fusion
Authors: Shi Sairu , Kong Shu , Zhang Qingwei , Zhang Ji 
Published: August 2023

AbstractMicrobial dysregulation may lead to the occurrence of diseases, and using microbe and drug data to infer the microbe-drug association has attracted extensive attention. There have been many studies to build association prediction models, but most of them are through biological experiments, which are time-consuming and expensive. Therefore, it is necessary to develop a computational method focusing on microbe-drug spatial information to predict the microbe-drug associations. In this work, we use the biological information to construct heterogeneous networks of drugs and microbes. We propose a new method based on Multiple Kernel fusion on Graph Attention Network (GAT) to predict human microbe-drug associations, called GATMDA. Our method extracts multi-layer features based on GAT which can learn the embedding of microbes and drugs on each layer and achieve the purpose of extracting multiple information. We further fuse multiple kernel matrices based on average weighting method. Finally, combined kernel in the microbe space and drug space are used to infer microbe-drug associations. Compared with eight state-of-the-art methods, our method receives the highest AUC and AUPR on MDAD and aBiofilm dataset. Case studies for Human immunodeficiency virus 1 (HIV-1) and adenovirus further confirm the effectiveness of GATMDA in identifying potential microbe-drug associations.
Auxin Exposure Disrupts Feeding Behavior and Fatty Acid Metabolism in AdultDrosophila
Authors: Fleck Sophie A. , Biswas Puja , DeWitt Emily D. , Knuteson Rebecca L. , Eisman Robert C. , Nemkov Travis , D’Alessandro Angelo , Tennessen Jason M. , Rideout Elizabeth J. , Weaver Lesley N. 
Published: August 2023

ABSTRACTThe ease of genetic manipulation inDrosophila melanogasterusing theGal4/UASsystem has been beneficial in addressing key biological questions. Current modifications of this methodology to temporally induce transgene expression require temperature changes or exposure to exogenous compounds, both of which have been shown to have detrimental effects on physiological processes. The recently described auxin-inducible gene expression system (AGES) utilizes the plant hormone auxin to induce transgene expression and is proposed to be the least toxic compound for genetic manipulation, with no obvious effects onDrosophiladevelopment and survival in one wild-type strain. Here we show that auxin delays larval development in another widely-used fly strain, and that short- and long-term auxin exposure in adultDrosophilainduces observable changes in physiology and feeding behavior. We further reveal a dosage response to adult survival upon auxin exposure, and that the recommended auxin concentration for AGES alters feeding activity. Furthermore, auxin fed male and female flies exhibit a significant decrease in triglyceride levels and display altered transcription of fatty acid metabolism genes. Although fatty acid metabolism is disrupted, auxin does not significantly impact adult female fecundity or progeny survival, suggesting AGES may be an ideal methodology for studying limited biological processes. These results emphasize that experiments using temporal binary systems must be carefully designed and controlled to avoid confounding effects and misinterpretation of results.
Automating an insect biodiversity metric using distributed optical sensors: an evaluation across Kansas, USA cropping systems
Authors: Rydhmer Klas , Eckberg James O. , Lundgren Jonathan G. , Jansson Samuel , Still Laurence , Quinn John E. , Washington Ralph , Lemmich Jesper , Nikolajsen Thomas , Sheller Nikolaj , Michels Alex M. , Bredeson Michael M. , Rosenzweig Steven T. , Bick Emily N. 
Published: August 2023

AbstractGlobal ecosystems and food supply depend on insect biodiversity for key functions such as pollination and decomposition. High-resolution, accurate data on invertebrate populations and communities across scales are critical for informing conservation efforts. However, conventional data collection methodologies for invertebrates are expensive, labor intensive, and require substantial taxonomic expertise, limiting researchers, practitioners, and policymakers. Novel optical techniques show promise for automating such data collection across scales as they operate unsupervised in remote areas. In this work, optical insect sensors were deployed in 20 agricultural fields in Kansas, USA. Measurements were compared to conventional assessments of insect diversity from sweep nets and Malaise traps. Species richness was estimated on optical insect data by applying a clustering algorithm to the optical insect sensor’s signal features of wing-beat frequency and body-to-wing ratio. Species richness correlated more strongly between the optical richness estimate and each of the conventional methods than between the two conventional methods, suggesting sensors can be a reliable indicator of invertebrate richness. Shannon- and Simpson indices were calculated for all three methods but were largely uncorrelated including between conventional methods. Although the technology is relatively new, optical sensors may provide next-generation insight into the spatiotemporal dynamics of invertebrate biodiversity and their conservation.Significance StatementThe implications of this research extend from the field level to the regional level. Much of what scientists understand about the decline of invertebrates comes from a small number of long-term studies that can be coarse and correlational in nature. High-resolution biodiversity data sets on fields to landscapes may provide the insight needed for the successful management and accounting of biodiversity by practitioners and policymakers. Such high-resolution data has the potential to support global efforts and coordination of biodiversity conservation.
In vitro toxicity of LiTFSI on Human Renal and Hepatoma Cells
Authors: Zhang Xing , Sands Mia , Lin Mindy , Guelfo Jennifer , Irudayaraj Joseph 
Published: August 2023

AbstractWe evaluate the cytotoxicity, intracellular redox conditions, apoptosis, and methylation of DNMTs/TETs upon exposure to LiTFSI, a novel PFAS compound commonly found in lithium-ion batteries, on human renal carcinoma cells (A498) and hepatoma cells (HepG2). The MTT assay showed both PFOS and LiTFSI had a dose-dependent effect on A498 and HepG2, with LiTFSI being less toxic. Intracellular redox conditions were assessed with a microplate reader and confocal, which showed a significant decrease in ROS levels and an increase in SOD content in both cells. Exposure to LiTFSI enhanced cell apoptosis, with HepG2 being more susceptible than A498. Quantitative analysis of mRNA expression levels of 19 genes associated with kidney injury, methylation, lipid metabolism and transportation was performed. LiTFSI exposure impacted kidney function by downregulating Acta2 and upregulating Tgfb1, Bcl2l1, Harvcr1, Nfe2l2, and Hes1 expression. LiTFSI exposure also affected the abundance of transcripts associated with DNA methylation by the expression of TET and DNMT genes. Furthermore, LiTFSI exposure induced an increase in lipid anabolism and alterations in lipid catabolism in HepG2. Our results provide new insight on the potential role of a new contaminant, LiTFSI in the regulation of oxidative stress, apoptosis and methylation in human renal carcinoma and hepatoma cells.
Hypoxia-inducible-factors differentially contribute to clinical disease and viral replication during RSV infection
Authors: Morris Dorothea R. , Qu Yue , Haas de Mello Aline , Jones-Hall Yava L. , Liu Tianshuang , Weglarz Meredith , Ivanciuc Teodora , Garofalo Roberto P. , Casola Antonella 
Published: August 2023

AbstractHypoxia-inducible-factors (HIF) are transcription factors that regulate cellular adaptation to hypoxic conditions, enabling cells to survive in low-oxygen environments. Viruses have evolved to activate this pathway to promote successful viral infection, therefore modulation of HIFs could represent a novel antiviral strategy. In previousin vitrostudies, we found that respiratory syncytial virus (RSV), a leading cause of respiratory illness, stabilizes HIFs under normoxic conditions, with inhibition of HIF-1α resulting in reduced viral replication. Despite several HIF modulating compounds being tested/approved for use in other non-infectious models, little is known about their efficacy against respiratory viruses using relevant animal models. This study aimed to characterize the disease modulating properties and antiviral potential of HIF-1α (PX478) and HIF-2α (PT2385) inhibitors in RSV-infected BALB/c mice. We found that inhibition of HIF-1α worsen clinical disease parameters, while simultaneously improving lung inflammation and airway function. Additionally, blocking HIF-1α resulted in significantly reduced viral titer at early and peak time points of RSV replication. In contrast, inhibition of HIF-2α was associated with improved clinical parameters, with no changes in airway function, enhanced immune responses and reduced early and peak lung viral replication. Analysis of lung cells found significant modification in the T-cell compartment that correlated with changes in lung pathology and viral titers in response to each HIF inhibitor administration. This study underscores the differential roles of HIF proteins in RSV infection and highlights the need for further characterization of the compounds that are currently in use or under therapeutic consideration.
Predicting host-based, synthetic lethal antiviral targets from omics data
Authors: Staheli Jeannette P. , Neal Maxwell L. , Navare Arti , Mast Fred D. , Aitchison John D. 
Published: August 2023

ABSTRACTTraditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.
Effect of Ambient Temperature on Diabetes Patient Visits at Primary and Referral Healthcare Services in Yogyakarta Province, Indonesia: An Analysis of Indonesian National Health Insurance Data 2021
Authors: Ramadona Aditya Lia , Qaimamunazzala Hayu , Kusnanto Hari , Lazuardi Lutfan , Danawati Cornelia Wahyu , Fuad Anis , Tetra Dewi Fatwa Sari 
Published: August 2023

ABSTRACTThe global incidence of diabetes mellitus is on the rise, posing a significant health challenge worldwide. Recent evidence indicates a possible link between diabetes mellitus and temperature. This study aimed to examine the effect of temperature on diabetes patient visits in Yogyakarta Province, Indonesia, which is currently experiencing a high burden of diabetes and an increasingly aging population. Daily temperature data for 2021 were obtained from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The number of diabetes patient visits was gathered from BPJS Health data samples for primary and referral healthcare facilities in the province. The relationship between temperature and diabetes patient visits was explored through data visualization, Pearson correlation, and Poisson regression models. We found a short-term correlation between higher temperatures and an increase in patient visits daily. However, observations on Sundays and holidays may not reflect the delayed effect of temperature on patient visits since not all primary and referral care providers offer services on these days. Nonetheless, including the observations on these days is necessary to assess the overall impact of temperature on patient visits weekly and monthly. The regression analysis revealed that for each additional 1°C increase in the average monthly temperature, the estimated number of patients for primary care increased by 15% and 14% for referral care services.