bluuebunny/crossref_metadata_2025_split huggingface dataset:
SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra
Authors: Han Xu , Wang Wanli , Ma Li-Hua , Al-Ramahi Ismael , Botas Juan , MacKenzie Kevin , Allen Genevera I. , Young Damian W. , Liu Zhandong , Maletic-Savatic Mirjana 
Published: February 2023

AbstractNuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained fromDrosophila melanogasterbrains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.
Matching Variants for functional characterization of genetic variants
Authors: Cevik Sebiha , Zhao Pei , Zorluer Atiyye , Bian Wenyin , Kaplan Oktay I. 
Published: February 2023

AbstractRapid and low-cost sequencing, as well as computer analysis, have facilitated the diagnosis of many genetic diseases, resulting in a substantial rise in the number of disease-associated genes. However, genetic diagnosis of many disorders remains problematic due to the lack of interpretation for many genetic variants, especially missenses, the infeasibility of high-throughput experiments on mammals, and the shortcomings of computational prediction technologies. Additionally, the available mutant databases are not well-utilized. Toward this end, we usedCaenorhabditis elegansmutant resources to delineate the functions of eight missense variants (V444I, V517D, E610K, L732F, E817K, H873P, R1105K, and G1205E) and two stop codons (W937stop and Q1434stop), including several matching variants (MatchVar) with human in ciliopathy associated IFT-140 (also called CHE-11)//IFT140 (intraflagellar transport protein 140). Moreover, MatchVars carryingC. elegansmutants, including IFT-140(G680S) and IFT-140(P702A) for the human (G704S) (dbSNP: rs150745099) and P726A (dbSNP: rs1057518064 and a conflicting variation) were created using CRISPR/Cas9. IFT140 is a key component of IFT complex A (IFT-A), which is involved in the retrograde transport of IFT along cilia and the entrance of G protein-coupled receptors (GPCRs) into cilia. Functional analysis of all ten variants revealed that P702A and W937stop, but not others phenocopied the ciliary phenotypes (short cilia, IFT accumulations, mislocalization of membrane proteins, and cilia entry of non-ciliary proteins) of the IFT-140 null mutant, indicating that both P702A and W937stop are phenotypic inC. elegans. Our functional data offered experimental support for interpreting human variants, by using ready-to-use mutants carrying MatchVars and generating MatchVars with CRISPR/Cas9.
Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens
Authors: Hassan Arshia Zernab , Ward Henry N. , Rahman Mahfuzur , Billmann Maximilian , Lee Yoonkyu , Myers Chad L. 
Published: February 2023

AbstractCRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods - autoencoders, robust, and classical principal component analyses (PCA) - for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel “onion” normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.
DOCK3 regulates normal skeletal muscle regeneration and glucose metabolism
Authors: Samani Adrienne , Karuppasamy Muthukumar , English Katherine G. , Siler Colin A. , Wang Yimin , Widrick Jeffrey J. , Alexander Matthew S. 
Published: February 2023

AbstractDOCK (dedicator of cytokinesis) is an 11-member family of typical guanine nucleotide exchange factors (GEFs) expressed in the brain, spinal cord, and skeletal muscle. Several DOCK proteins have been implicated in maintaining several myogenic processes such as fusion. We previously identified DOCK3 as being strongly upregulated in Duchenne muscular dystrophy (DMD), specifically in the skeletal muscles of DMD patients and dystrophic mice.Dock3ubiquitous KO mice on the dystrophin-deficient background exacerbated skeletal muscle and cardiac phenotypes. We generatedDock3conditional skeletal muscle knockout mice (Dock3mKO) to characterize the role of DOCK3 protein exclusively in the adult muscle lineage.Dock3mKO mice presented with significant hyperglycemia and increased fat mass, indicating a metabolic role in the maintenance of skeletal muscle health.Dock3mKO mice had impaired muscle architecture, reduced locomotor activity, impaired myofiber regeneration, and metabolic dysfunction. We identified a novel DOCK3 interaction with SORBS1 through the C-terminal domain of DOCK3 that may account for its metabolic dysregulation. Together, these findings demonstrate an essential role for DOCK3 in skeletal muscle independent of DOCK3 function in neuronal lineages.
Neutrophil-specific STAT4 deficiency attenuates atherosclerotic burden and improves plaque stability via reduction in neutrophil activation and recruitment into aortas ofLdlr-/-mice
Authors: Keeter W. Coles , Moriarty Alina K , Akers Rachel , Ma Shelby , Mussbacher Marion , Nadler Jerry L. , Galkina Elena V. 
Published: February 2023

AbstractBackground and AimsNeutrophils drive atheroprogression and directly contribute to plaque instability. We recently identified signal transducer and activator of transcription 4 (STAT4) as a critical component for bacterial host defense in neutrophils. The STAT4-dependent functions of neutrophils in atherogenesis are unknown. Therefore, we investigated a contributory role of STAT4 in neutrophils during advanced atherosclerosis.MethodsWe generated myeloid-specificStat4ΔLysMLdlr-/-, neutrophil-specificStat4ΔS100A8Ldlr-/-, and controlStat4fl/flLdlr-/-mice. All groups were fed a high-fat/cholesterol diet (HFD-C) for 28 weeks to establish advanced atherosclerosis. Aortic root plaque burden and stability were assessed histologically by Movat Pentachrome staining. Nanostring gene expression analysis was performed on isolated blood neutrophils. Flow cytometry was utilized to analyze hematopoiesis and blood neutrophil activation.In vivohoming of neutrophils to atherosclerotic plaques was performed by adoptively transferring prelabeledStat4ΔLysMLdlr-/-andStat4fl/flLdlr-/-bone marrow cells into aged atheroscleroticApoe-/-mice and detected by flow cytometry.ResultsSTAT4 deficiency in both myeloid-specific and neutrophil-specific mice provided similar reductions in aortic root plaque burden and improvements in plaque stability via reduction in necrotic core size, improved fibrous cap area, and increased vascular smooth muscle cell content within the fibrous cap. Myeloid-specific STAT4 deficiency resulted in decreased circulating neutrophils via reduced production of granulocyte-monocyte progenitors in the bone marrow. Neutrophil activation was dampened inStat4ΔLysMLdlr-/-mice via reduced mitochondrial superoxide production, attenuated surface expression of degranulation marker CD63, and reduced frequency of neutrophil-platelet aggregates. Myeloid-specific STAT4 deficiency diminished expression of chemokine receptors CCR1 and CCR2 and impairedin vivoneutrophil trafficking to atherosclerotic aorta.ConclusionsOur work indicates a pro-atherogenic role for STAT4-dependent neutrophil activation and how it contributes to multiple factors of plaque instability during advanced atherosclerosis in mice.
Machine-learning and mechanistic modeling of primary and metastatic breast cancer growth after neoadjuvant targeted therapy
Authors: Benzekry S. , Mastri M. , Nicolò C. , Ebos J. ML 
Published: February 2023

AbstractClinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors prior to surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet such trials are rarely preceded by preclinical testing involving surgery. Here we used a mouse model of spontaneous metastasis after surgical removal to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Longitudinal data consisted of measurements of presurgical primary tumor size and postsurgical metastatic burden in 128 mice (104 for model training, 24 for validation), following variable neoadjuvant treatment schedules over a 14-day period. A nonlinear mixed-effects modeling approach was used to quantify inter-animal variability. Machine learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our simulations showed that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery. Surprisingly, machine-learning algorithms demonstrated only limited predictive power of tested biomarkers on the mathematical parameters. These results suggest that presurgical modeling might be an effective tool to screen biomarkers prior to clinical trial testing. Mathematical modeling combined with artificial intelligence techniques represent a novel platform for integrating preclinical surgical metastasis models in outcome prediction of neoadjuvant treatment.Major findingsUsing simulations from a mechanistic mathematical model compared with preclinical data from surgical metastasis models, we found anti-tumor effects of neoadjuvant RTKI treatment can differ between the primary tumor and metastases in the perioperative setting. Model simulations with variable drug doses and scheduling of neoadjuvant treatment revealed a contrasting impact on initial primary tumor debulking and metastatic outcomes long after treatment has stopped and tumor surgically removed. Using machine-learning algorithms, we identified the limited power of several circulating cellular and molecular biomarkers in predicting metastatic outcome, uncovering a potential fast-track strategy for assessing future clinical biomarkers by paring patient studies with identical studies in mice.
The cryptic step in biogeochemical Tellurium (Te) cycle: Indirect elementary Te oxidation mediated by manganese-oxidizing bacterium (MnOB)
Authors: Liu Yuqing , Ma Huiqing , Li Ang , Yi Xianliang , Liu Yang , Zhan Jingjing , Zhou Hao 
Published: February 2023

AbstractTellurium (Te) is a rare element in the chalcogen group, and its biogeochemical cycle has been investigated for decades. As the most soluble Te species, tellurite (Te(IV)) possess the highest toxicity to the organisms. Chemical or biological Te(IV) reduction to elemental tellurium (Te0) is generally considered as an effective detoxification route for Te(IV)-containing wastewater. Here, we reported a previously overlooked Te0oxidation process mediated by manganese-oxidizing bacteriumBacillussp. FF-1. This strain has both Mn(II)-oxidizing and Te(IV)-reducing activities, which could produce manganese oxides (BioMnOx) and Te0(BioTe0) when incubating with Mn(II) and Te(IV), respectively. Te(IV) can co-precipitated with Mn(II) to form highly stable Te(IV)-Mn(II) compounds with low bioavailability. While when 5 mM Mn(II) was added after incubating 0.1 mM or 1 mM Te(IV) with strain FF-1 for 16 hours, the BioTe0were certainly re-oxidized to Te(IV) by BioMnOx according to the results of X-ray photoelectron spectra (XPS) and Transmission electron microscope (TEM). The chemogenic and exogenous biogenic Te0can also be oxidized by the BioMnOx, although with different rates. This study highlights a new transformation process of tellurium species mediated by manganese-oxidizing bacteria, revealing that the environmental fate and ecological risks of Te0needed to be re-evaluated.ImportanceBiogeochemical cycle of Te mediated by bacteria mainly focus on the Tellurite reduction and methylation. In this study, the indirect tellurium (Te0) oxidation driven by manganese-oxidizing bacterium is firstly confirmed. As Te0 usually considered as a stable and safe products during Te(IV)-containing wastewater treatment, we suppose the ecological risks of Te0needed to be re-evaluated due to the possible oxidation by manganese-oxidizing bacterium and its generated manganese oxides.
Ensemble deep learning of embeddings for clustering multimodal single-cell omics data
Authors: Yu Lijia , Liu Chunlei , Yang Jean Yee Hwa , Yang Pengyi 
Published: February 2023

AbstractMotivationRecent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy.ResultsWe propose SnapCCESS for clustering cells by integrating data modalities in multimodal singlecell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterisation of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data.Availability and implementationSnapCCESS is implemented as a Python package and is freely available fromhttps://github.com/yulijia/SnapCCESS.
Biological Age Estimation Using Circulating Blood Biomarkers
Authors: Bortz Jordan , Guariglia Andrea , Klaric Lucija , Tang David , Ward Peter , Geer Michael , Chadeau-Hyam Marc , Vuckovic Dragana , Joshi Peter K 
Published: February 2023

ABSTRACTBiological Age (BA) captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for BA estimation. This study aims to improve BA estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (UKBB) (n = 307,000). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk, which outperforms the well-known blood-biomarker based PhenoAge model, providing a 9.2% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. BA is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of BA, available to the general population.
Cannabis use and atherosclerotic cardiovascular disease: a Mendelian randomization study
Authors: de La Harpe Roxane , Schoeler Tabea , Thorball Christian W , Thomas Aurélien , Kutalik Zoltán , Vaucher Julien 
Published: February 2023

AbstractBackgroundResearch on the link between cannabis use and the development of atherosclerotic cardiovascular disease (ASCVD) is inconsistent and challenging to interpret, given existing study limitations.AimsTo estimate the effects of genetically indexed cannabis use on the risk of coronary artery disease (CAD) and acute ischemic stroke (IS).Methods65 independent single-nucleotide polymorphisms (SNPs), obtained from a genome-wide association study on lifetime cannabis use (n=184,765), were employed as instruments to estimate the association between genetically indexed cannabis use and risk of CAD and IS using a two-sample Mendelian Randomization (MR) approach. Summary statistics on CAD (CARDIoGRAMplusC4D Consortium; 60,801 cases and 123,504 controls) and IS (MEGASTROKE; 34,217 cases and 406,111 controls) were obtained separately. A comprehensive review of the observational literature on cannabis use and CAD or IS was also performed and contrasted with MR results.ResultsThere was no causal effect of cannabis use on the risk of CAD (odds ratio (OR) per ever-users vs. never-users 0.93; 95% confidence interval (CI), 0.83 to 1.03) or IS (OR 1.05; 95%CI, 0.93 to 1.19). Sensitivity analyses yielded similar results, and no heterogeneity and directional pleiotropy were observed. Our meta-analysis of observational studies showed no significant association between ever use of cannabis with risk of CAD (k=6 studies; ORpooled=1.23, 95% CI 0.78 to 1.69), nor with IS (k=6 studies; ORpooled=1.22, 95% CI 0.95 to 1.50).ConclusionUsing a genetic approach approximating a clinical trial revealed no evidence for a causal effect of genetic predisposition to cannabis use on CAD or IS development. These findings are reassuring from a public health perspective, as ever cannabis use is unlikely to contribute to the risk of ASCVD.
Spatial transcriptomics maps molecular and cellular requirements for CD4+T cell-dependent immunity to malaria
Authors: Williams Cameron G. , Moreira Marcela L. , Asatsuma Takahiro , Skinner Oliver P. , Lee Hyun Jae , Li Shihan , Barrera Irving , Murray Evan , Soon Megan S. F. , Engel Jessica A. , Khoury David S. , Asad Saba , Steiner Thiago Mass , Joseph Rainon , Alexandre Yannick , Mueller Scott N. , Chen Fei , Haque Ashraful 
Published: February 2023

AbstractCD4+T cells orchestrate adaptive immunity to circulating malaria parasites; yet cellular interactions and molecular mechanisms controlling Th1 and Tfh differentiation in the spleen remain to be fully definedin vivo. Here, using a murine model of CD4-dependent immunity, we tested ifSlide-seqV2, a spatial transcriptomic method with near single-cell resolution, could determine the locations of multiple CD4+T cell subsets and potentially interacting cellular partners in the spleen during infection. Firstly,Slide-seqV2readily mapped splenic cellular structure and microanatomical change during infection. Next, computational integration with scRNA-seq reference datasets of splenocytes, stromal cells, and specifically of polyclonal CD4+T cells and B cells, mapped the relative locations of multiple cell-types within this dense tissue. scRNA-seq of B cells over time mapped emergence of germinal centre B cells, red pulp-located plasmablasts and atypical B cells, and uncovered a prolonged CD4+T-cell-independent, follicular bystander B cell response marked by Sca-1 and Ly6C upregulation. scRNA-seq of activated, polyclonal CD4+T cells revealed their similarity to our previous TCR transgenic models. Importantly, spatial analysis revealed polyclonal Th1 cells co-localised with CXCL9/10-producing monocytes in the red pulp, while polyclonal Tfh-like cells were located close to CXCL13-expressing B cell follicles, consistent with our previous CXCR3/CXCR5 competition model of Th1/Tfh bifurcation. CRISPR/Cas9 disruption of either or both CXCR3 and CXCR5 in naïvePlasmodium-specific CD4+T cells had unexpectedly minor effects on Th1 differentiationin vivo. Instead, CXCR5 was essential for maximising clonal expansion, suggesting a role for splenic CXCL13+cells in supporting CD4+T cell proliferation in malaria. Thus, spatial transcriptomics at near single-cell resolution was feasible in densely packed secondary lymphoid tissue, providing multiple insights into mechanisms controlling splenic polyclonal CD4+T cell and B cell differentiation during infection.HighlightsSlide-seqV2maps splenic microanatomy, including stromal and immune cell location.Bystander activation of all follicular B cells occurs in malaria, marked by Sca-1/Ly6C upregulation.Single naïve polyclonal CD4+T cells differentiate mostly into Th1 and Tfh cells in malaria.Cell-cell colocalization analysis positions Th1 cells with monocytes in red pulp, and Tfh cells withCxcl13+B cell follicles.CXCR5, but not CXCR3, supports parasite-specific CD4+T cell clonal expansion.