bluuebunny/crossref_metadata_2025_split huggingface dataset:
Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in absence of growth
Authors: Rens Elisabeth G. , Zeegers Mathé T. , Rabbers Iraes , Szabó Andras , Merks Roeland M.H. 
Published: May 2020

AbstractEpithelial branching morphogenesis drives the development of organs such as the lung, salivary gland, kidney and the mammary gland. It involves cell proliferation, cell differentiation and cell migration. An elaborate network of chemical and mechanical signals between the epithelium and the surrounding mesenchymal tissues regulates the formation and growth of branching organs. Surprisingly, when cultured in isolation from mesenchymal tissues, many epithelial tissues retain the ability to exhibit branching morphogenesis even in absence of proliferation. In this work, we propose a simple, experimentally-plausible mechanism that can drive branching morphogenesis in absence of proliferation and cross-talk with the surrounding mesenchymal tissue. The assumptions of our mathematical model derive fromin vitroobservations of the behavior of mammary epithelial cells. These data show that autocrine secretion of the growth factor TGFβ1 inhibits the formation of cell protrusions, leading to curvature dependent inhibition of sprouting. Our hybrid cellular Potts and partial-differential equation model correctly reproduces the experimentally observed tissue-geometry dependent determination of the sites of branching, and it suffices for the formation of self-avoiding branching structures in absence and also in presence of cell proliferation.
Analyzing editosome function in high-throughput
Authors: Del Campo Cristian , Leeder Wolf-Matthias , Reißig Paul , Göringer H. Ulrich 
Published: May 2020

AbstractMitochondrial gene expression in African trypanosomes and other trypanosomatid pathogens requires a U-nucleotide specific insertion/deletion-type RNA-editing reaction. The process is catalyzed by a macromolecular protein complex known as the editosome. Editosomes are restricted to the trypanosomatid clade and since editing is essential for the parasites, the protein complex represents a near perfect target for drug intervention strategies. Here we report the development of an improvedin vitroassay to monitor editosome function. The test system utilizes fluorophore-labeled substrate RNAs to analyze the processing reaction by automated, high-throughput capillary electrophoresis (CE) in combination with a laser-induced fluorescence (LIF) readout. We optimized the assay for high-throughput screening (HTS)-experiments and devised a multiplex fluorophore-labeling regime to scrutinize the U-insertion/U-deletion reaction simultaneously. The assay is robust, it requires only nanogram amounts of materials and it meets all performance criteria for HTS-methods. As such the test system should be helpful in the search for trypanosome-specific pharmaceuticals.
Theory of mechano-chemical patterning and optimal migration in cell monolayers
Authors: Boocock Daniel , Hino Naoya , Ruzickova Natalia , Hirashima Tsuyoshi , Hannezo Edouard 
Published: May 2020

AbstractCollective cell migration offers a rich field of study for non-equilibrium physics and cellular biology, revealing phenomena such as glassy dynamics [1], pattern formation [2] and active turbulence [3]. However, how mechanical and chemical signaling are integrated at the cellular level to give rise to such collective behaviors remains unclear. We address this by focusing on the highly conserved phenomenon of spatio-temporal waves of density [2, 4–8] and ERK/MAPK activation [9–11], which appear bothin vitroandin vivoduring collective cell migration and wound healing. First, we propose a biophysical theory, backed by mechanical and optogenetic perturbation experiments, showing that patterns can be quantitatively explained by a mechano-chemical coupling between three-dimensional active cellular tensions and the mechano-sensitive ERK/MAPK pathway. Next, we demonstrate how this biophysical mechanism can robustly induce migration in a desired orientation, and we determine a theoretically optimal pattern for inducing efficient collective migration fitting well with experimentally observed dynamics. We thereby provide a bridge between the biophysical origin of spatio-temporal instabilities and the design principles of robust and efficient long-ranged migration.
Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma
Authors: Zaritsky Assaf , Jamieson Andrew R. , Welf Erik S. , Nevarez Andres , Cillay Justin , Eskiocak Ugur , Cantarel Brandi L. , Danuser Gaudenz 
Published: May 2020

AbstractDeep convolutional neural networks have emerged as a powerful technique to identify hidden patterns in complex cell imaging data. However, these machine learning techniques are often criticized as uninterpretable “black-boxes” - lacking the ability to provide meaningful explanations for the cell properties that drive the machine’s prediction. Here, we demonstrate that the latent features extracted from label-free live cell images by an adversarial auto-encoding deep convolutional neural network capture subtle details of cell appearance that allow classification of melanoma cell states, including the metastatic efficiency of seven patient-derived xenograft models that reflect clinical outcome. Although trained exclusively on patient-derived xenograft models, the same classifier also predicted the metastatic efficiency of immortalized melanoma cell lines suggesting that the latent features capture properties that are specifically associated with the metastatic potential of a melanoma cell regardless of its origin. We used the autoencoder to generate “in-silico” cell images that amplified the cellular features driving the classifier of metastatic efficiency. These images unveiled pseudopodial extensions and increased light scattering as functional hallmarks of metastatic cells. We validated this interpretation by analyzing experimental image time-lapse sequences in which melanoma cells spontaneously transitioned between states indicative of low and high metastatic efficiency.Together, this data is an example of how the application of Artificial Intelligence supports the identification of processes that are essential for the execution of complex integrated cell functions but are too subtle to be identified by a human expert.
IgA MAb blocks SARS-CoV-2 Spike-ACE2 interaction providing mucosal immunity
Authors: Ejemel Monir , Li Qi , Hou Shurong , Schiller Zachary A. , Wallace Aaron L. , Amcheslavsky Alla , Yilmaz Nese Kurt , Toomey Jacqueline R. , Schneider Ryan , Close Brianna J. , Chen Da-Yuan , Conway Hasahn L. , Mohsan Saeed , Cavacini Lisa A. , Klempner Mark S. , Schiffer Celia A. , Wang Yang 
Published: May 2020

SummaryCOVID-19 caused by SARS-CoV-2 has become a global pandemic requiring the development of interventions for the prevention or treatment to curtail mortality and morbidity. No vaccine to boost mucosal immunity or as a therapeutic has yet been developed to SARS-CoV-2. In this study we discover and characterize a cross-reactive human IgA monoclonal antibody, MAb362. MAb362 binds to both SARS-CoV and SARS-CoV-2 spike proteins and competitively blocks hACE2 receptor binding, by completely overlapping the hACE2 structural binding epitope. Furthermore, MAb362 IgA neutralizes both pseudotyped SARS-CoV and SARS-CoV-2 in human epithelial cells expressing hACE2. SARS-CoV-2 specific IgA antibodies, such as MAb362, may provide effective immunity against SARS-CoV-2 by inducing mucosal immunity within the respiratory system, a potentially critical feature of an effective vaccine.
Peptide YY: a novel Paneth cell antimicrobial peptide that maintains fungal commensalism
Authors: Pierre Joseph F. , La Torre Diana , Sidebottom Ashley , Kambal Amal , Zhu Xiaorong , Tao Yun , Cham Candace M. , Wang Ling , Harris Katharine G. , Zaborina Olga , Alverdy John , Herzog Herbert , Witchley Jessica , Noble Suzanne M. , Leone Vanessa , Chang Eugene B. 
Published: May 2020

SUMMARYPerturbed interactions between the intestinal microbes and host correlate with emergence of fungal virulence. Here we report a previously unknown role for peptide YY (PYY), a described endocrine molecule, as an antimicrobial peptide (AMP) expressed by gut immune epithelial Paneth Cells (PC). PC-PYY differs from other AMPs, including lysozyme, because of limited antibacterial activity, packaging in discrete secretory granules, and selective antifungal activity to virulent hyphae, but not yeast forms ofCandida albicans. The latter action is through binding of cationic PC-PYY to the anionic hyphal surface, resulting in membrane disruption and killing. PC-PYY is compartmentalized to surface mucus, which optimizes activity and prevents conversion to endocrine PYY by dipeptidyl peptidase-IV (DPP-IV). We conclude PC-PYY is a unique AMP with selective antifungal activity that maintains gut fungal commensalism. Compromised PC-PYY action from PC dysfunction and/or mucus depletion in ileal Crohn’s disease may initiate or contribute to disease via fungal pathogenesis.Highlights⍰ Paneth Cell PYY (PC-PYY) is an antimicrobial peptide that differs from endocrine-PYY⍰ PC-PYY is a selective anti-fungal peptide, targeting the virulent form of C.albicans⍰ PC-PYY is separately packaged, retained by mucus, and released byC. albicanshyphae⍰ PC-PYY is proposed as essential for maintenance of fungal commensalism in the gutGraphical AbstractModel for Paneth cell (PC) PYY action and regulation of fungal commensalisms and potential role in the pathogenesis of ileal Crohn’s Disease (iCD)(A)In a healthy ileum, commensal yeast reside and do not stimulate PYY1-36release from PCs.(B)Increased virulent hyphae (purple hyphae) results in PYY1-36release from crypt PCs into the mucus. Hyphae are targeted by PYY1-36and killed (red hyphae) to manage the increased fungi community in gut.(C)In a diseased ileum such as iCD, hyphal load induces immune activation and increased inflammation through PC dysfunction (gray PCs) and decreased PYY1-36release or mucus depletion and PC dysfunction.
StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants
Authors: Sharo Andrew G. , Hu Zhiqiang , Sunyaev Shamil R. , Brenner Steven E. 
Published: May 2020

AbstractBackgroundWhole genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and structural variant detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing.ResultsTo address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity.ConclusionsWe anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in patients where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is available athttps://compbio.berkeley.edu/proj/strvctvre/.
Understanding olfactory dysfunction in COVID-19: Expression of ACE2, TMPRSS2 and Furin in the nose and olfactory bulb in human and mice
Authors: Ueha Rumi , Kondo Kenji , Kagoya Ryoji , Shichino Shigeyuki , Ueha Satoshi , Yamasoba Tatsuya 
Published: May 2020

AbstractBackgroundAnosmia is a frequent symptom in coronavirus disease 2019 (COVID-19) patients that generally resolves within weeks. In contrast, the anosmia caused by other upper respiratory infections affects a small proportion of patients and may take months to resolve or never resolve. The mechanisms behind COVID-19-induced olfactory dysfunction remain unknown. Here, we address the unique pathophysiology of COVID-19-associated olfactory dysfunction.MethodsThe expression of ACE2 (virus binding receptor) and TMPRSS2 and Furin (host cell proteases facilitating virus entry) was examined in the nasal mucosa, composed of respiratory mucosa (RM), olfactory mucosa (OM), and olfactory bulb (OB) of mouse and human tissues using immunohistochemistry and gene analyses.ResultsCo-expression of ACE2, TMPRSS2, and Furin was observed in the RM and in the OM, especially in the supporting cells of the olfactory epithelium and the Bowman’s glands. Notably, the olfactory receptor neurons (ORNs) in the OM were positive for ACE2 but almost negative for TMPRSS2 and Furin. Cells in the OB expressed ACE2 strongly and Furin weakly, and did not express TMPRSS2. All three gene expressions were confirmed in the nasal mucosa and OB.ConclusionsACE2 was widely expressed in all tissues, whereas TMPRSS2 and Furin were expressed only in certain types of cells and were absent in the ORNs. These findings, together with clinical reports, suggest that COVID-19-related anosmia occurs mainly through sensorineural and central dysfunction and, to some extent, conductive olfactory dysfunction. The expression of ACE2, but not TMPRSS2 or Furin, in ORNs may explain the early recovery from anosmia.
Traffic-derived particulate matter and angiotensin-converting enzyme 2 expression in human airway epithelial cells
Authors: Miyashita L , Foley G , Semple S , Grigg J 
Published: May 2020

AbstractBackgroundThe mechanism for the association between traffic-derived particulate matter less than 10 microns (PM10) and cases of COVID-19 disease reported in epidemiological studies is unknown. To infect cells, the spike protein of SARS-CoV-2 interacts with angiotensin-converting enzyme 2 (ACE2) on host airway cells. Increased ACE2 expression in lower airway cells in active smokers, suggests a potential mechanism whereby PM10increases vulnerability to COVID-19 disease.ObjectiveTo assess the effect of traffic-derived PM10on human airway epithelial cell ACE2 expressionin vitro.MethodsPM10was collected from Marylebone Road (London) using a kerbside impactor. A549 and human primary nasal epithelial cells were cultured with PM10for 2 h, and ACE2 expression (median fluorescent intensity; MFI) assessed by flow cytometry. We included cigarette smoke extract as a putative positive control. Data were analysed by either Mann-Whitney test, or Kruskal-Wallis with Dunn’s multiple comparisons test.ResultsPM10at 10 μg/mL, and 20 μg/mL increased ACE2 expression in A549 cells (P<0.05, 0.01 vs. medium control, respectively). Experiments using a single PM10concentration (10 μg/mL), found increased ACE2 expression in both A549 cells (control vs. PM10, median (IQR) MFI; 470 (0.1 to 1114) vs 6217 (5071 to 8506), P<0.01), and in human primary epithelial cells (0 (0 to 591) vs. 4000 (2610 to 7853), P<0.05). Culture of A549 cells with 5% cigarette smoke extract increased ACE2 expression (n=4, 0 (0 to 28) vs. 9088 (7557 to 15831, P<0.05).ConclusionTraffic-related PM10increases the expression of the receptor for SARS-CoV-2 in human respiratory epithelial cells.
Aberrant claudin-6–adhesion signal promotes endometrial cancer progression via estrogen receptor α
Authors: Kojima Manabu , Sugimoto Kotaro , Tanaka Mizuko , Endo Yuta , Ichikawa-Tomikawa Naoki , Kashiwagi Korehito , Kato Hitomi , Honda Tsuyoshi , Furukawa Shigenori , Nishiyama Hiroshi , Watanabe Takafumi , Soeda Shu , Fujimori Keiya , Chiba Hideki 
Published: May 2020

AbstractCell adhesion proteins not only maintain tissue integrity but also possess signaling abilities to organize diverse cellular events in physiological and pathological processes; however, the underlying mechanism remains obscure. Among cell adhesion molecules, the claudin (CLDN) family often possesses aberrant expression in various cancers, but the biological relevance and molecular basis have not yet been established. Here, we show that high CLDN6 expression promotes endometrial cancer progression and represents the poor prognostic marker. The second extracellular domain and Y196/200 of CLDN6 were required to recruit and activate Src-family kinases (SFKs) and to stimulate malignant phenotypes. Importantly, we demonstrate that the CLDN6/SFK/PI3K-dependent AKT and SGK (serum- and glucocorticoid-regulated kinase) signalings target Ser518 in the human estrogen receptor α and ligand-independently activate target genes in endometrial cancer cells, resulting in cancer development. The identification of this machinery highlights regulation of the transcription factors by cell adhesion to advance tumor progression.
Pathway-based and phylogenetically adjusted quantification of metabolic interaction between microbial species
Authors: Lam Tony J. , Stamboulian Moses , Han Wontack , Ye Yuzhen 
Published: May 2020

AbstractMicrobial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of metabolic interactions, such as competition and cooperation, between bacterial species. By nature, phylogenetically similar microbial species are likely to share common functional profiles or biological pathways due to their genomics similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation based on functional/pathway profiles may bias downstream applications.To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species.Author summaryMicrobial communities, also known as microbiomes, are formed through the interactions of various microbial species. Utilizing genomic sequencing, it is possible to infer the compositional make-up of communities as well as predict their metabolic interactions. However, because some species are more similarly related to each other, while others are more distantly related, one cannot directly compare metabolic relationships without first accounting for their phylogenetic relatedness. Here we developed a computational pipeline which predicts complimentary and competitive metabolic relationships between bacterial species, while normalizing for their phylogenetic relatedness. Our results show that phylogenetic distances are correlated with metabolic interactions, and factoring out such relationships can help better understand microbial interactions which drive community formation.
Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models
Authors: Kaden Marika , Bohnsack Katrin Sophie , Weber Mirko , Kudła Mateusz , Gutowska Kaja , Blazewicz Jacek , Villmann Thomas 
Published: May 2020

AbstractWe present an approach to investigate SARS-CoV-2 virus sequences based on alignment-free methods for RNA sequence comparison. In particular, we verify a given clustering result for the GISAID data set, which was obtained analyzing the molecular differences in coronavirus populations by phylogenetic trees. For this purpose, we use alignment-free dissimilarity measures for sequences and combine them with learning vector quantization classifiers for virus type discriminant analysis and classification. Those vector quantizers belong to the class of interpretable machine learning methods, which, on the one hand side provide additional knowledge about the classification decisions like discriminant feature correlations, and on the other hand can be equipped with a reject option. This option gives the model the property of self controlled evidence if applied to new data, i.e. the models refuses to make a classification decision, if the model evidence for the presented data is not given. After training such a classifier for the GISAID data set, we apply the obtained classifier model to another but unlabeled SARS-CoV-2 virus data set. On the one hand side, this allows us to assign new sequences to already known virus types and, on the other hand, the rejected sequences allow speculations about new virus types with respect to nucleotide base mutations in the viral sequences.Author summaryThe currently emerging global disease COVID-19 caused by novel SARS-CoV-2 viruses requires all scientific effort to investigate the development of the viral epidemy, the properties of the virus and its types. Investigations of the virus sequence are of special interest. Frequently, those are based on mathematical/statistical analysis. However, machine learning methods represent a promising alternative, if one focuses on interpretable models, i.e. those that do not act as black-boxes. Doing so, we apply variants of Learning Vector Quantizers to analyze the SARS-CoV-2 sequences. We encoded the sequences and compared them in their numerical representations to avoid the computationally costly comparison based on sequence alignments. Our resulting model is interpretable, robust, efficient, and has a self-controlling mechanism regarding the applicability to data. This framework was applied to two data sets concerning SARS-CoV-2. We were able to verify previously published virus type findings for one of the data sets by training our model to accurately identify the virus type of sequences. For sequences without virus type information (second data set), our trained model can predict them. Thereby, we observe a new scattered spreading of the sequences in the data space which probably is caused by mutations in the viral sequences.