bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic flux fingerprinting differentiates planktonic and biofilm states ofPseudomonas aeruginosaandStaphylococcus aureus
Authors: Lichtenberg Mads , Kragh Kasper Nørskov , Fritz Blaine , Bier-Kirkegaard Julius , Bjarnsholt Thomas 
Published: July 2020

AbstractThe challenges of defining the biofilm phenotype has been clear for decades. Many biomarkers for biofilm are known, but methods for identifying these are often invasive and/or complicated. These methods often rely on disrupting the biofilm matrix or examining virulence factors and compounds, which may only be expressed under certain conditions.We used microcalorimetric measurements of metabolic energy release to investigate whether unchallenged, planktonicPseudomonas aeruginosadisplayed differences in metabolism compared to surface-bound and non-attached biofilms.The pattern of energy release observed in the recorded microcalorimetric thermograms clearly depended on growth state, though the total energy expenditure was not different between growth states. To characterize these differences, we developed a classification pipeline utilizing machine learning algorithms to classify growth state, based on the observed patterns of energy release. With this approach, we could with high accuracy detect the growth form of blinded samples. To challenge the algorithm, we attempted to limit the amount of training data. By training the algorithm with only a single data point from each growth form, we obtained a mean accuracy of 90.5% using two principal components. Further validation of the classification pipeline showed that the approach was not limited toP. aeruginosabut could also be used for detection of gram-positiveStaphylococcus aureusbiofilm. We propose that microcalorimetric measurements, in combination with this new quantitative framework, can be used as a non-invasive biomarker to detect the presence of biofilm.These results could have a significant potential in clinical settings where the detection of biofilms in infections often means a different outcome and treatment regime for the patient.
EM-stellar: benchmarking deep learning for electron microscopy image segmentation
Authors: Khadangi Afshin , Boudier Thomas , Rajagopal Vijay 
Published: July 2020

AbstractThe inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. We present EM-stellar, a Jupyter Notebook platform that is hosted on google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-Stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics.
Transcriptome profiling reveals the mechanism of ripening and epidermal senescence in passion (Passiflora edulia Sims) fruit
Authors: Li Changbao , Xin Ming , Li Li , He Xuemei , Liu Guomin , Li Jiemin , Sheng Jinfeng , Sun Jian 
Published: July 2020

AbstractPassion fruit (Passiflora edulia Sims), an important tropical and sub-tropical species, is classified as a respiration climacteric fruit, the quality deteriorates rapidly after harvest. To reveal the mechanisms involved in ripening and rapidly fruit senescence, the phytochemical characteristics and RNA sequencing were conducted in the purple passion fruits with different (1-MCP and PF) treatment. Comprehensive functional annotation and KEGG enrichment analysis showed that the starch and sucrose metabolism, plant hormone signal transduction, phenylpropanoid biosynthesis, flavonid biosynthesis, carotenoid biosynthesis were involved in fruit ripening. Applying with PF and 1-MCP significantly affected transcript levels of passion fruit after harvest storage. A large number of differently expressed unigenes (DEGs) were identified significantly enrichen in starch and sucrose metabolism, plant hormone signal transduction and phenylpropanoid biosynthesis at postharvest stage. The preservative film (PF) and 1-Methylcyclopropene (1-MCP) treatments increased superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) gene expression and enzyme activities, accelerated the lignin accumulation, decline β-galactosidase (β-Gal), polygalacturonase (PG) and cellulose activities and gene expression to delay cell wall degradation during fruit senescence. The RNA sequencing data of cell wall metabolism and hormone signal transduction pathway related unigenes were verified by RT-qPCR. The results indicated that the cell wall metabolism and hormone signal pathways were notably related to passion fruit ripening. PF and 1-MCP treatment might inhibited ethylene signaling and regulated cell wall metabolism pathways to inhibited cell wall degradation. Our results reveal ripening and senescence related networks during passion fruit ripening, which can provide a foundation for understanding the molecular mechanisms underlying PF and 1-MCP treatment on fruit ripening.
Small molecule resensitizesSalmonellato itaconate and decreases bacterial proliferation in macrophages
Authors: Duncan Dustin , Chang Justin H. , Artyomov Maxim N. , Auclair Karine 
Published: July 2020

AbstractAntimicrobial resistance is a global health crisis offering little reprieve. The situation urgently calls for new drug targets and therapies for infections. We have previously suggested a different approach to treat infections, termed bacterio-modulation, in which a compound modulates the bacterial response to the host immune defense. Herein we show that monocytes infected withSalmonella entericaspp. Typhimurium can be cured using non-antimicrobial compounds that resensitize the bacterium to itaconate, a macrophage-derived antimicrobial metabolite. We propose that this represents a novel strategy to treat infections.
Consequences of a short-term exposure to a sub lethal concentration of CdO nanoparticles on key life history traits in the fruit fly (Drosophila melanogaster)
Authors: El Kholy Samar , Giesy John P. , Al Naggar Yahya 
Published: July 2020

AbstractNanoparticles of cadmium oxide (CdO NPs) are among the most common industrial metal oxide nanoparticles. Early adulthood (F0) fruit flies (D. melanogaster) were exposed for 7 days to a sub lethal concentration (0.03 mg CdO NPs/ml, which was 20% of the LC50), spiked into food media to test for long term-effects over time and beyond their direct exposure on key life history traits. Effects on survival, developmental time, eclosion rate, fecundity and negative geotaxis performance were assessed. Potential effects on ultrastructure of mid gut cells were also investigated by use of electron microscopy. All studied life history traits, as well as climbing behavior were adversely affected by exposure to CdO NPs. In non-exposed progeny (F1) of adult flies (F0), a blistered wing phenotype was also observed. Lysis of nuclear and rough endoplasmic reticulum (rER) membranes, mitochondrial swelling and lysis were among the most common cellular alterations observed in midgut cells of F0flies exposed to CdO NPs. Genes encoding for metallothionein (MTn A-D) were significantly upregulated in both parent flies (F0) and their progeny (F1) after exposure of F0flies to CdO NPs, compared to unexposed, control flies, a result which indicated potential, long-term effects. Taken together, these results suggest that short-term exposure to a sublethal concentration of CdO NPs is sufficient to cause long-lasting, harmful effects on fruit flies.Graphical Abstract
A direct RNA-protein interaction atlas of the SARS-CoV-2 RNA in infected human cells
Authors: Schmidt Nora , Lareau Caleb A. , Keshishian Hasmik , Melanson Randy , Zimmer Matthias , Kirschner Luisa , Ade Jens , Werner Simone , Caliskan Neva , Lander Eric S. , Vogel Jörg , Carr Steven A. , Bodem Jochen , Munschauer Mathias 
Published: July 2020

ABSTRACTSARS-CoV-2 infections pose a global threat to human health and an unprecedented research challenge. Among the most urgent tasks is obtaining a detailed understanding of the molecular interactions that facilitate viral replication or contribute to host defense mechanisms in infected cells. While SARS-CoV-2 co-opts cellular factors for viral translation and genome replication, a comprehensive map of the host cell proteome in direct contact with viral RNA has not been elucidated. Here, we use RNA antisense purification and mass spectrometry (RAP-MS) to obtain an unbiased and quantitative picture of the human proteome that directly binds the SARS-CoV-2 RNA in infected human cells. We discover known host factors required for coronavirus replication, regulators of RNA metabolism and host defense pathways, along with dozens of potential drug targets among direct SARS-CoV-2 binders. We further integrate the SARS-CoV-2 RNA interactome with proteome dynamics induced by viral infection, linking interactome proteins to the emerging biology of SARS-CoV-2 infections. Validating RAP-MS, we show that CNBP, a regulator of proinflammatory cytokines, directly engages the SARS-CoV-2 RNA. Supporting the functional relevance of identified interactors, we show that the interferon-induced protein RYDEN suppresses SARS-CoV-2 ribosomal frameshifting and demonstrate that inhibition of SARS-CoV-2-bound proteins is sufficient to manipulate viral replication. The SARS-CoV-2 RNA interactome provides an unprecedented molecular perspective on SARS-CoV-2 infections and enables the systematic dissection of host dependency factors and host defense strategies, a crucial prerequisite for designing novel therapeutic strategies.
Lithium ameliorates Cornelia de Lange syndrome associated phenotypes in experimental models
Authors: Grazioli Paolo , Parodi Chiara , Mariani Milena , Bottai Daniele , Di Fede Elisabetta , Zulueta Aida , Avagliano Laura , Cereda Anna , Tenconi Romano , Wierzba Jolanta , Adami Raffaella , Iascone Maria , Ajmone Paola Francesca , Vaccari Thomas , Gervasini Cristina , Selicorni Angelo , Massa Valentina 
Published: July 2020

ABSTRACTCornelia de Lange Syndrome (CdLS) is a rare developmental disorder affecting a multitude of organs including the central nervous system, inducing a variable neurodevelopmental delay. CdLS malformations derive from deregulation of developmental pathways, inclusive of the canonical WNT pathway.We have evaluated MRI anomalies and behavioral and neurological clinical manifestations in CdLS patients. Importantly, we observed in our cohort a significant association between behavioral disturbance and structural abnormalities in brain structures of hindbrain embryonic origin.Considering the cumulative evidence on the cohesin-WNT-hindbrain shaping cascade, we have explored possible ameliorative effects of chemical activation of the canonical WNT pathway with lithium chloride in different models: (I)Drosophila melanogasterCdLS model showing a significant rescue of mushroom bodies morphology in the adult flies; (II) mouse neural stem cells restoring physiological levels in proliferation rate and differentiation capabilities toward the neuronal lineage; (III) lymphoblastoid cell lines from CdLS patients and healthy donors restoring cellular proliferation rate and inducing the expression ofCyclinD1. This work supports a role for WNT-pathway regulation of CdLS brain and behavioral abnormalities and a consistent phenotype rescue by lithium in experimental models.
Inhibition of ITK differentiates GVT and GVHD in allo-HSCT
Authors: Mammadli Mahinbanu , Huang Weishan , Harris Rebecca , Sultana Aisha , Cheng Ying , Tong Wei , Pu Jeffery , Gentile Teresa , Henty-Ridilla Jessica , Dsouza Shanti , Yang Qi , August Avery , Bah Alaji , Karimi Mobin 
Published: July 2020

AbstractAllogeneic hematopoietic stem cell transplantation is a life-saving treatment for many malignant and nonmalignant diseases. Donor T cells contained within the graft prevent tumor recurrenceviagraft-versus-tumor (GVT) effects, however, also cause graft-versus-host disease (GVHD). Novel treatment strategies are therefore needed to allow maintenance of GVT while suppressing GVHD. Here we show using murine models, that targeting IL-2-inducible T cell kinase (ITK) in donor T cells reduces GVHD while preserving the beneficial GVT effects. Donor T cells fromItk-/-mice exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs such as liver and small intestine, while maintaining GVT efficacy against primary B-ALL tumors.Itk-/-T cells exhibited reduced expression of IRF4 and decreased JAK/STAT signaling activity, but preserved cytotoxicity, which was accompanied by upregulation of Eomesodermin (Eomes), which was necessary for GVT function. A novel peptide inhibitor ITK signaling is also able to prevent GVHD. This novel peptide inhibitor also reduced cytokine production in mice and human T cells. Altogether, our data suggest that inhibiting ITK could be a therapeutic strategy to reduce GVHD while preserving the beneficial GVT effects following allo-HSCT treatment.Key PointsInhibiting ITK by a novel peptide significantly reduces GVHD but retains GVT.ITK deficient donor T cells exhibit minimal GVHD, but maintain GVT activity.ITK deficient donor T cells exhibit significantly reduced production of inflammatory cytokines and migration to GVHD target organs.Eomes is required for GVT effect.
A deep learning approach for staging embryonic tissue isolates with small data
Authors: Pond Adam , Hwang Seongwon , Verd Berta , Steventon Benjamin 
Published: July 2020

AbstractMachine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.Author summaryThe application of machine learning approaches currently hinges on the availability of large data sets to train the models with. However, recent research has shown that large data sets might not always be required. In this work we set out to see whether we could use small confocal microscopy image data sets to train a convolutional neural network (CNN) to stage zebrafish tail buds at four different stages in their development. We found that high test accuracies can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a CNN. This work also shows that we can robustly stage the embryonic development of isolated structures, without the need to refer back to landmarks in the tail bud. This constitutes an important methodological advance for staging organoids and other 3D culture in vitro systems. This work proves that prohibitively large data sets are not always required to train CNNs, and we hope will encourage others to apply the power of machine learning to their areas of study even if data are scarce.
Receptor-like role for PQLC2 amino acid transporter in the lysosomal sensing of cationic amino acids
Authors: Talaia Gabriel , Amick Joseph , Ferguson Shawn M. 
Published: July 2020

ABSTRACTPQLC2, a lysosomal cationic amino acid transporter, also serves as a sensor that responds to scarcity of its substrates by recruiting a protein complex comprised of C9orf72, SMCR8 and WDR41 to the surface of lysosomes. This protein complex controls multiple aspects of lysosome function. Although it is known that this response to changes in cationic amino acid availability depends on an interaction between PQLC2 and WDR41, the underlying mechanism for the regulated interaction is not known. In this study, we establish that the WDR41-PQLC2 interaction is mediated by a short peptide motif in a flexible loop that extends from the WDR41 β-propeller and inserts into a cavity presented by the inward-facing conformation of PQLC2. This data supports a transceptor model wherein conformational changes in PQLC2 related to substrate transport regulate the availability of the WDR41 binding site on PQLC2 and mediate recruitment of the WDR41-SMCR8-C9orf72 complex to the surface of lysosomes.
IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza A virus
Authors: Yin Rui , Thwin Nyi Nyi , Zhuang Pei , Zhang Yu , Lin Zhuoyi , Kwoh Chee Keong 
Published: July 2020

AbstractThe rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we introduce a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combing the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.
TRPM7 is a critical regulator of pancreatic endocrine development and high-fat diet-induced β-cell proliferation
Authors: Altman Molly K. , Schaub Charles M. , Dickerson Matthew T. , Dadi Prasanna K. , Graff Sarah M. , Galletta Thomas J. , Amarnath Gautami , Thorson Ariel S. , Gu Guoqiang , Jacobson David A. 
Published: July 2020

ABSTRACTThe melastatin subfamily of the transient receptor potential channels (TRPM) are regulators of pancreatic β-cell function. TRPM7 is the most abundant islet TRPM channel; however, the role of TRPM7 in β-cell function has not been determined. Here, we utilized various spatiotemporal transgenic mouse models to investigate how TRPM7 knockout influences pancreatic endocrine development, proliferation, and function. Ablation of TRPM7 within pancreatic progenitors reduced pancreatic size, as well as α-cell and β-cell mass. This resulted in impaired glucose tolerance due to decreased serum insulin levels. However, ablation of TRPM7 following endocrine specification or in adult mice did not impact endocrine expansion or glucose tolerance. As TRPM7 regulates cell proliferation, we assessed how TRPM7 influences β-cell hyperplasia under insulin resistant conditions. β-cell proliferation induced by high-fat diet was significantly decreased in TRPM7 deficient β-cells. The endocrine roles of TRPM7 may be influenced by cation flux through the channel, and indeed we find that TRPM7 ablation alters β-cell intracellular Mg2+. Together, these findings reveal that TRPM7 controls pancreatic progenitor expansion and β-cell proliferation, which is likely due to regulation of Mg2+homeostasis.SummaryThis manuscript identifies a critical developmental role for TRPM7 channels in pancreatic progenitor cells. The manuscript also determines that TRPM7 plays a key role in β-cell proliferation under insulin-resistant conditions.