bluuebunny/crossref_metadata_2025_split huggingface dataset:
SETD2 Deficiency Promotes Inflammatory Bowel Disease via Oxidative Stress and FasL-induced Apoptosis
Authors: Wang Yueduo , Shen Shenghai , Li Li 
Published: November 2022

AbstractSET-domain-containing 2 (SETD2) is known as the major trimethyltransferase that regulates the methylation of histone H3 lysine 36 (H3K36), which has been found frequently mutated in IBD samples. Although SETD2 deficiency has been proven to modulate oxidative stress, the specific mechanisms of SETD2 in IBD are still little known. To investigate the possible role of SETD2 in IBD, we generated and genotyped epithelium-specific deletion ofSetd2(Setd2Vil-KO) progeny mice to establish mice IBD pathology model and then studied molecular expression differences at mRNA and protein levels in the intestinal epithelial tissue ofSetd2Vil-KOmice. Compared withSetd2F/Fmice, the tissue ofSetd2Vil-KOmice showed increases in oxidative stress and cell apoptosis level via RT-qPCR, while TUNEL assay and immunofluorescence showed significantly enhanced apoptotic signaling and FasL expression. Together, our findings highlight that SETD2 regulates oxidative stress and FasL-induced apoptosis inSetd2Vil-KOmice intestinal epithelial cells through epigenetics mechanisms, which plays an essential role in IBD pathogenesis. Therefore, our study provides new insights into the prevention and therapy of IBD from the perspective of epigenetic regulation.
Latch Verified Bulk-RNA Seq toolkit: a cloud-based suite of workflows for bulk RNA-seq quality control, analysis, and functional enrichment
Authors: Le Hannah G.B.H. , Steenwyk Jacob L. , Manske Nathan , Smolin Max , Abdulali Aidan , Kamat Ayush , Kanchana Rohan , Giffin Kyle , Andere Alfredo , Workman Kenny 
Published: November 2022

AbstractBackgroundAnalysis of high-throughput bulk RNA-sequencing (RNA-seq) data reveals changes in gene expression between diverse conditions. Many tools have emerged to quality control RNA-seq reads, quantify expression levels, conduct functional enrichment among differentially expressed genes, or identify differential RNA splicing. However, unified toolkits for conducting these analyses are lacking. Moreover, existing software does not use cloud-based platforms that provide the necessary storage and computational resources to process RNA-seq data or intuitive graphical interfaces for easy use by experimental and computational scientists.ResultsTo address these challenges, we introduce the Latch Verified Bulk RNA-Seq (LVBRS) toolkit, a flexible suite of programs packaged into a single workflow coupled with a graphical user interface for conducting quality control, transcript quantification, differential splicing, differential expression analysis, and functional enrichment analyses. For functional enrichment, the LVBRS toolkit supports three databases—Gene Ontology, KEGG Pathway, and Molecular Signatures database—capturing diverse functional information. We demonstrate the utility of the LVBRS toolkit by reanalyzing a publicly available dataset examining the impact of severe and mild models of hypoxia—induced by Cobalt (II) Chloride (CoCl2) and oxyquinoline treatment, respectively—on a human colon adenocarcinoma cell line. Our analyses reveal CoCl2treatment results in more differentially expressed genes, recapitulating previously reported results that CoCl2models more severe hypoxia. Moreover, including alternative splicing and functional enrichment analysis using a greater breadth of functional databases revealed additional biological insights—such as greater alternative splicing in the CoCl2condition and differentially expressed DNA repair pathways. These results demonstrate the LVBRS toolkit’s efficacy in facilitating biological insights from bulk RNA-seq data.ConclusionsThe LVBRS toolkit offers a robust unified framework for processing and analyzing Bulk RNA-Seq experiments. The easy-to-use graphical user interface will enable diverse scientists to conduct high-throughput bulk RNA-Seq analysis efficiently. Our aim is that the LVBRS toolkit will help streamline bulk RNA-seq workflows and facilitate deriving biologically meaningful insights from bulk RNA-seq data. The source code is freely available under the MIT license and hosted on the LatchBio Console (https://console.latch.bio/se/bulk-rnaseq), complete with documentation (https://latch.wiki/bulk-rna-seq-end-to-end).
STREAK: A Supervised Cell Surface Receptor Abundance Estimation Strategy for Single Cell RNA-Sequencing Data using Feature Selection and Thresholded Gene Set Scoring
Authors: Javaid Azka , Frost H. Robert 
Published: November 2022

AbstractThe accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on three joint scRNA-seq/CITE-seq datasets that represent two human tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.
The structure of Lepidoptera-plant interaction networks across clades, life stages, and environmental gradients
Authors: Ho Hsi-Cheng , Altermatt Florian 
Published: November 2022

AbstractAimIntegrate biogeographic and ecological knowledge to understand the spatial-structural patterns of plant-insect interaction networks at the landscape scale.LocationThe 36,000 km2German state of Baden-Württemberg, Central Europe.MethodsWe integrated extensive data of Lepidoptera-plant occurrences and interactions to inferentially construct local interaction networks across Baden-Württemberg, considering in total 3148 plant and 980 Lepidoptera species, covering butterflies, Noctuid moths, Geometrid moths and Bombycoid moths. We quantified clade- and life-stage-specific network structures and related these features to GIS-informed environmental conditions, thereby revealing the spatial (environmental) patterns and potential drivers of networks’ structural variation across the landscape.ResultsSpanning the same environmental gradients, Lepidoptera clades and life stages can form various interaction structures with food plants and exhibit distinct spatial-structural patterns. For all major Lepidopteran groups, except Geometrid moths, potential diet across life stages tended to broaden toward low-elevation farmlands. The larval and adult networks of butterflies became less modular with farmland coverage; the same for adult Noctuid moths, but the inverse for adult Geometrid moths. With increasing elevation, the larval and adult networks of Noctuid moths became less and more modular, respectively, whereas Geometrid adult networks became more modular. While the adult dietary niche of butterflies was more overlapped at low elevation, those of Noctuid and Geometrid moths further associated with land cover and were more overlapped toward low- and high-elevation farmlands, respectively.Main conclusionsEnvironmental factors and biotic interactions together shape ecological communities. By particularly accounting for species-interaction contexts, we revealed the spatial-structural patterns of Lepidoptera-plant networks along geo-climate and land-cover gradients, where the shaping mechanisms likely include both evolutionary (e.g., resource-consumer co-evolution) and ecological (e.g., competitive exclusion) processes and are specific to Lepidoptera’s clade or life stage. Such biogeographical structural patterns provide ecological and conservation implications at both species and community levels, and can indicate the potential response of Lepidoptera-plant communities to environmental changes.
Bladder-draining lymph nodes support germinal centre B cell responses during urinary tract infection in mice
Authors: Hawas Sophia , Vagenas Dimitrios , Haque Ashraful , Totsika Makrina 
Published: November 2022

AbstractBacterial urinary tract infections (UTIs) are both common and exhibit high recurrence rates in women. UTI healthcare costs are increasing due to the rise of multi-drug resistant (MDR) bacteria, necessitating alternative approaches for infection control. Here, we investigated whether host adaptive immune responses can influence infection outcomes. We employed a mouse model in which wild-type C57BL/6J mice were transurethrally inoculated with an MDR UTI strain of uropathogenicEscherichia coli(UPEC). Firstly, we noted thatrag1-/-C57BL/6J mice harboured larger bacterial burdens than wild-type counterparts, consistent with a role for T and/or B cells in optimal control of UTI. Consistent with this, UTI triggered in the bladders of wild-type mice early increases of myeloid cells, including CD11chiconventional dendritic cells, suggesting possible involvement of these professional antigen-presenting cells. Importantly, germinal centre (GC) B cell responses developed by 4 weeks post-infection in bladder-draining lymph nodes of wild-type mice, and although modest in magnitude and transient in nature, could not be boosted with a second UTI. Thus, our data reveal for the first time in a mouse model, that Gram-negative bacterial UTI induces local B cell immune responses in bladder-draining lymph nodes, which could potentially serve to control infection.
Estimating the Incidence of Familial Exudative Vitreoretinopathy (FEVR) in The State of Michigan
Authors: Trese Matthew G.J. , Qu Xianggui , Drenser Kimberly A. 
Published: November 2022

AbstractBackground and ObjectiveAmongst ophthalmologists, familial exudative vitreoretinopathy (FEVR) is agreed to be a rare disease. However, its incidence is currently unknown. The goal of this study is to identify the incidence of FEVR in Michigan.Study Design/Materials and MethodsThis retrospective chart review evaluated new patient encounters from the largest retinal specialty practice within the State of Michigan from over a 6 year period. These identified encounters were queried for the international classification of disease 10 (ICD-10) code (H35.023): exudative retinopathy, not diabetes. Review of both the individual patient encounters and the appropriate clinical imaging resulted in the identification of our FEVR cohort which was subsequently compared with the live birthrate in the State of Michigan over the same 6 years to determine an incidence rate.ResultsThis study revealed 44 new FEVR diagnosis and 666, 102 livebirths over the 6 year period of interest. Thus, the incidence of FEVR in the state of Michigan was estimated to be 0.007%. (95% CI[0.005%- 0.009%)ConclusionFamilial Exudative Vitreoretinopathy is indeed a rare diagnosis with an incidence rate of 0.007% within the State of Michigan.
Transparency in infectious disease research: a meta-research survey of specialty journals
Authors: Zavalis Emmanuel A. , Contopoulos-Ioannidis Despina G. , Ioannidis John P.A. 
Published: November 2022

AbstractIntroductionInfectious diseases carry a large global burden and have implications for society at large. Therefore, reproducible, transparent research is extremely important. To assess the current state of transparency in this field, we investigated code sharing, data sharing, protocol registration, conflict of interest and funding disclosures in articles published in the most influential infectious disease journals.MethodsWe evaluated transparency indicators in the 5340 PubMed Central Open Access (PMC OA) articles published in 2019 or 2021 in the 9 most-cited specialty journals in infectious disease. We used a previously validated text-mining R package,rtransparent. The approach was manually validated for a random sample of 200 articles for which study characteristics were also extracted in detail. Main comparisons assessed 2019 versus 2021 articles, 2019 versus 2021 non-COVID-19 articles, and 2021 non-COVID-19 articles versus 2021 COVID-19 articles.ResultsA total of 5340 articles were evaluated (1860 published in 2019 and 3480 in 2021 (of which 1828 on COVID-19)). Text-mining identified code sharing in 98 (2%) articles, data sharing in 498 (9%), registration in 446 (8%), conflict of interest disclosures in 4209 (79%) and funding disclosures in 4866 (91%). There were substantial differences across the 9 journals in the proportion of articles fulfilling each transparency indicator: 1-9% for code sharing, 5-25% for data sharing, 1-31% for registration, 7-100% for conflicts of interest, and 65-100% for funding disclosures. There were no major differences between articles published in 2019 and non-COVID-19 articles in 2021. In 2021, non-COVID-19 articles had more data sharing (12%) than COVID-19 articles (4%). Validation-corrected imputed estimates were 3% for code sharing, 11% for data sharing, 8% for registrations, 79% for conflict of interest disclosures and 92% for funding disclosures.ConclusionData sharing, code sharing, and registration are very uncommon in infectious disease specialty journals. Increased transparency is required.
Integrative analysis of GWAS and co-localisation data suggests novel genes associated with age-related multimorbidity
Authors: West Clare E. , Karim Mohd , Falaguera Maria J. , Speidel Leo , Green Charlotte J. , Logie Lisa , Schwartzentruber Jeremy , Ochoa David , Lord Janet M. , Ferguson Michael A. J. , Bountra Chas , Wilkinson Graeme F. , Vaughan Beverley , Leach Andrew R. , Dunham Ian , Marsden Brian D. 
Published: November 2022

AbstractAdvancing age is the greatest risk factor for developing multiple age-related diseases. When developing therapeutics, using a Geroscience approach to target the shared underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while potentially reducing the burden of polypharmacy. We harness the Open Targets Platform and Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits to identify genetic signals that appear to be shared between two or more of these traits. We identify 995 targets with shared genetic links to these age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable by small molecule or antibody modality approaches, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets in a web application, TargetAge, to enable the exploration and prioritisation of possible novel drug targets for age-related multimorbidity.
A Junction-Dependent Mechanism Drives Mammary Cell Intercalation for Ductal Elongation
Authors: Pfannenstein Alexander , Macara Ian G. 
Published: November 2022

AbstractMammary glands contain branched networks of ducts and alveoli that function to produce milk for offspring. While the murine luminal epithelium is organized as a cellular monolayer, it originates from multilayered structures called terminal end buds (TEB). The TEBs generate ducts of monolayered epithelial cells as they invade the fat pad, but little is known about underlying mechanisms. While apoptosis provides a plausible mechanism for cavitation of the ductal lumen, it does not account for elongation of ducts behind the TEBs. Our spatial calculations suggest that most cells in TEBs need to intercalate into the outermost luminal layer and that this migration of cells is the primary driver of cavitation and ductal elongation. To study the progression of multilayered to monolayered epithelium, we developed a quantitative cell culture assay that determines the efficiency of intercalation into an epithelial monolayer. Using this tool, we verified that loss of adherens junctions prevents stable integration of cells into monolayers, consistent with previous data in cultured cells and in primary tissue. Interestingly, tight junction (TJ) proteins also play a key role in this integration process. Although loss of the ZO-1 TJ protein in intercalating cells suppresses intercalation, loss of ZO-1 in the monolayer has the reverse effect, promoting intercalation – even though ZO-1 is not necessary for establishment of TJs. ZO-1-positive puncta form between cells and the monolayer, which then resolves into a new intercellular boundary as intercalation proceeds. ZO-1 loss also reduces engraftment when cells are transplanted into the mammary gland via intraductal injection. We further show that intercalation is dependent on dynamic cytoskeletal rearrangements in both the existing monolayer and intercalating cells. These data identify luminal cell rearrangements necessary for mammary gland development and suggest a molecular mechanism for integration of cells into an existing monolayer.
DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data
Authors: Yang Wenyi , Xu Zhaochun , Luo Meng , Cai Yideng , Xu Chang , Wang Pingping , Wei Songren , Xue Guangfu , Jin Xiyun , Cheng Rui , Que Jinhao , Zhou Wenyang , Pang Fenglan , Nie Huan , Jiang Qinghua 
Published: November 2022

AbstractWith the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (CCIs) from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. To address the issue, here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively.
A DNA origami fiducial for accurate 3D AFM imaging
Authors: Kolbeck Pauline J. , Dass Mihir , Martynenko Irina V. , van Dijk-Moes Relinde J.A. , Brouwer Kelly J.H. , van Blaaderen Alfons , Vanderlinden Willem , Liedl Tim , Lipfert Jan 
Published: November 2022

ABSTRACTAtomic force microscopy (AFM) is a powerful technique for imaging molecules, macromolecular complexes, and nanoparticles with nanometer-resolution. However, AFM images are distorted by the shape of the tip used. These distortions can be corrected if the tip shape can be determined by scanning a sample with features sharper than the tip and higher than the object of interest. Here we present a 3D DNA origami structure as fiducial for tip reconstruction and image correction. Our fiducial is stable under a broad range of conditions and has sharp steps at different heights that enable reliable tip reconstruction from as few as ten fiducials. The DNA origami is readily co-deposited with biological and non-biological samples, achieves higher precision for the tip apex than polycrystalline samples, and dramatically improves the accuracy of the lateral dimensions determined from the images. Our fiducial thus enables accurate and precise AFM imaging for a broad range of applications.
Interleukin-11 causes alveolar type 2 cell dysfunction and prevents alveolar regeneration
Authors: Ng Benjamin , Huang Kevin Y. , Pua Chee Jian , Lim Wei-Wen , Kuthubudeen Fathima , Hii An An , Viswanathan Sivakumar , Petretto Enrico , Cook Stuart A. 
Published: November 2022

AbstractFollowing lung injury, alveolar regeneration is characterized by the transformation of alveolar type 2 (AT2) cells, via a transitional KRT8+ state, into alveolar type 1 (AT1) cells. In lung disease, dysfunctional intermediate cells accumulate, AT1 cells are diminished and fibrosis occurs. Using single cell RNA sequencing datasets of human interstitial lung disease, we found that interleukin-11 (IL11) is specifically expressed in aberrant KRT8 expressing KRT5-/KRT17+ and basaloid cells. Stimulation of AT2 cells with IL11 or TGFβ1 caused EMT, induced KRT8+ and stalled AT1 differentiation, with TGFβ1 effects being IL11 dependent. In bleomycin injured mouse lung, IL11 was increased in AT2-derived KRT8+ cells and deletion ofIl11ra1in lineage labeled AT2 cells reduced KRT8+ expression, enhanced AT1 differentiation and promoted alveolar regeneration, which was replicated in therapeutic studies using anti-IL11. These data show that IL11 maintains AT2 cells in a dysfunctional transitional state, impairs AT1 differentiation and blocks alveolar regeneration across species.TeaserInterleukin-11 stalls type 2-to-type 1 alveolar epithelial cell differentiation and prevents lung regeneration
A Deep Denoising Sound Coding Strategy for Cochlear Implants
Authors: Gajecki Tom , Zhang Yichi , Nogueira Waldo 
Published: November 2022

AbstractCochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their ability to understand speech degrades drastically when background interfering noise is present. To address this problem, current CI systems are delivered with front-end speech enhancement modules that can aid the listener in noisy environments. However, these only perform well under certain noisy conditions, leaving quite some room for improvement in more challenging circumstances. In this work, we propose replacing the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end speech denoising by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns applied to the electrodes across time. We specifically introduce a DNN that emulates a common CI sound coding strategy, the advanced combination encoder (ACE). We refer to the proposed algorithm as ‘Deep ACE’. Deep ACE is designed not only to accurately code the acoustic signals in the same way that ACE would but also to automatically remove unwanted interfering noises, without sacrificing processing latency. The model was optimized using a CI-specific loss function and evaluated using objective measures as well as listening tests in CI participants. Results show that, based on objective measures, the proposed model achieved higher scores when compared to the baseline algorithms. Also, the proposed deep learning-based sound coding strategy gave eight CI users the highest speech intelligibility results.