bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishment of multiple novel patient-derived models of desmoplastic small round cell tumor enabling functional characterization of ERBB pathway signaling and pre-clinical evaluation of a novel targeted therapy approach
Authors: Smith Roger S. , Odintsov Igor , Liu Zebing , Lui Allan Jo-Weng , Hayashi Takuo , Vojnic Morana , Suehara Yoshiyuki , Delasos Lukas , Mattar Marissa S. , Hmeljak Julija , Ramirez Hillary A. , Shaw Melissa , Bui Gabrielle , Hartono Alifiani B. , Gladstone Eric , Kunte Siddharth , Magnan Heather , Khodos Inna , De Stanchina Elisa , La Quaglia Michael P. , Yao Jinjuan , Laé Marick , Lee Sean B. , Spraggon Lee , Pratilas Christine A. , Ladanyi Marc , Somwar Romel 
Published: September 2020

AbstractDesmoplastic small round cell tumor (DSRCT) is characterized by the t(11;22)(p13;q12) chromosomal translocation, which fuses the transcriptional regulatory domain ofEWSR1with the zinc finger DNA-binding domain ofWT1, resulting in the oncogenic transcription factor EWS-WT1. DSRCT primarily affects young males and has a 5-year overall survival of about 15%. Typical treatment approaches for patients with DSRCT involve a multi-modal combination of surgery, chemotherapy and radiation. The paucity of DSRCT disease models has hampered functional and pre-clinical therapeutic studies in this aggressive cancer. Here, we developed robust preclinical disease models and mined DSRCT expression profiling data to identify genetic vulnerabilities that could be leveraged for the identification of rational therapies. Specifically, we developed four new DSRCT cell lines and one patient-derived xenograft (PDX) model. Transcriptomic and proteomic profiling showed evidence of activation of the ERBB pathway. Ectopic expression of EWSR1-WT1 resulted in upregulation of ERRB family ligands and downstream signaling. Treatment of DSRCT cell lines with ERBB ligands resulted in activation of EGFR, ERBB2, ERK1/2 and AKT, and stimulation of cell growth. Conversely, targeting of EGFR using shRNA, small molecule inhibitors (afatinib, neratinib) or an anti-EGFR antibody (cetuximab) inhibited growth and induced apoptosis in DSRCT cells. Finally, treatment of mice bearing DSRCT xenografts with a combination of cetuximab and afatinib significantly reduced tumor growth. These data provide a rationale for the clinical evaluation of EGFR antagonists in patients with DSRCT.
Prospective comparison of saliva and nasopharyngeal swab sampling for mass screening for COVID-19
Authors: Nacher Mathieu , Mergeay-Fabre Mayka , Blanchet Denis , Benois Orelie , Pozl Tristan , Mesphoule Pauline , Sainte-Rose Vincent , Vialette Véronique , Toulet Bruno , Moua Aurélie , Saout MONA , Simon Stéphane , Guidarelli Manon , Galindo Muriel , Biche Barbara , Faurous William , Abad Fanny , Fahrasmane Aniza , Rochemont Devi , Vignier Nicolas , Vabret Astrid , Demar Magalie 
Published: September 2020

AbstractCurrent testing for COVID-19 relies on quantitative reverse-transcriptase polymerase chain reaction from a nasopharyngeal swab specimen. Saliva samples have advantages regarding ease and painlessness of collection, which does not require trained staff and may allow self-sampling. We enrolled 776 persons at various field-testing sites and collected nasopharyngeal and pooled saliva samples. 162 had a positive COVID-19 RT-PCR, 61% were mildly symptomatic and 39% asymptomatic. The sensitivity of RT-PCR on saliva samples versus nasopharygeal swabs varied depending on the patient groups considered or on Ct thresholds. There were 10 (6.2%) patients with a positive saliva sample and a negative nasopharyngeal swab, all of whom had Ct values<25. For symptomatic patients for whom the interval between symptoms onset and sampling was <10 days sensitivity was 77% but when excluding persons with isolated Ngen positivity (54/162), sensitivity was 90%. In asymptomatic patients, the sensitivity was only 24%. When we looked at patients with Cts <30, sensitivity was 83% or 88.9% when considering 2 genes. The relatively good performance for patients with low Cts suggests that Saliva testing could be a useful and acceptable tool to identify infectious persons in mass screening contexts, a strategically important task for contact tracing and isolation in the community.
Longitudinal analysis of T-cell receptor repertoires reveals persistence of antigen-driven CD4+ and CD8+ T-cell clusters in Systemic Sclerosis
Authors: Servaas N.H. , Zaaraoui-Boutahar F. , Wichers C.G.K. , Ottria A. , Chouri E. , Affandi A.J. , Silva-Cardoso S. , van der Kroef M. , Carvalheiro T. , van Wijk F. , Radstake T.R.D.J. , Andeweg A.C. , Pandit A. 
Published: September 2020

AbstractThe T-cell receptor (TCR) is a highly polymorphic surface receptor that allows T-cells to recognize antigenic peptides presented on the major histocompatibility complex (MHC). Changes in the TCR repertoire have been observed in several autoimmune conditions, and these changes are suggested to predispose autoimmunity. Multiple lines of evidence have implied an important role for T-cells in the pathogenesis of Systemic Sclerosis (SSc), a complex autoimmune disease. One of the major questions regarding the roles of T-cells is whether expansion and activation of T-cells observed in the diseases pathogenesis is (auto)antigen driven.To investigate the temporal TCR repertoire dynamics in SSc, we performed high-throughput sequencing of CD4+ and CD8+ TCRβ chains on longitudinal samples obtained from four SSc patients collected over a minimum of two years. Repertoire overlap analysis revealed that samples taken from the same individual over time shared a high number of TCRβ sequences, indicating a clear temporal persistence of the TCRβ repertoire in CD4+ as well as CD8+ T-cells. Moreover, the TCRβs that were found with a high frequency at one time point were also found with a high frequency at the other time points (even after almost four years), showing that frequencies of dominant TCRβs are largely consistent over time. We also show that TCRβ generation probability and observed TCR frequency are not related in SSc samples, showing that clonal expansion and persistence of TCRβs is caused by antigenic selection rather than convergent recombination. Moreover, we demonstrate that TCRβ diversity is lower in CD4+ and CD8+ T-cells from SSc patients compared to healthy memory T-cells, as SSc TCRβ repertoires are largely dominated by clonally expanded persistent TCRβ sequences. Lastly, using “Grouping of Lymphocyte Interactions by Paratope Hotspots” (GLIPH2), we identify clusters of TCRβ sequences with homologous sequences that potentially recognize the same antigens and contain TCRβs that are persist in SSc patients.In conclusion, our results show that that CD4+ and CD8+ T-cells are highly persistent in SSc patients over time, and this persistence is likely a result from antigenic selection. Moreover, persistent TCRs form high similarity clusters with other (non-)persistent sequences, that potentially recognize the same epitopes. These data provide evidence for an (auto-)antigen driven expansion of CD4/CD8+ T-cells in SSc.
Role of IgG against N-protein of SARS-CoV2 in COVID19 clinical outcomes
Authors: Batra Mayank , Tian Runxia , Zhang Chongxu , Clarence Emile , Sacher Camila Sofia , Miranda Justin Nestor , De La Fuente Justin Rafa O , Mathew Megan , Green Desmond , Patel Sayari , Bastidas Maria Virginia Perez , Haddadi Sara , Murthi Mukunthan , Gonzalez Miguel Santiago , Kambali Shweta , Santos Kayo H M , Asif Huda , Modarresi Farzaneh , Faghihi Mohammad , Mirsaeidi Mehdi 
Published: September 2020

AbstractThe Nucleocapsid Protein (N Protein) of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV2) is located in the viral core. Immunoglobulin G (IgG) targeting N protein is detectable in the serum of infected patients. The effect of high titers of IgG against N-protein on clinical outcomes of SARS-CoV2 disease has not been described. We studied 400 RT-PCR confirmed SARS-CoV2 patients to determine independent factors associated with poor outcomes, including MICU admission, prolonged MICU stay and hospital admissions, and in-hospital mortality. We also measured serum IgG against the N protein and correlated its concentrations with clinical outcomes. We found that several factors, including Charlson comorbidity Index (CCI), high levels of IL6, and presentation with dyspnea were associated with poor clinical outcomes. It was shown that higher CCI and higher IL6 levels were independently associated with in-hospital mortality. Anti-N protein IgG was detected in the serum of 55 (55%) patients at the time of admission. A high concentration of antibodies, defined as signal to cut off ratio (S/Co)> 1.5 (75 percentile of all measurements), was found in 25 (25%) patients. The multivariable logistic regression models showed that between being an African American, higher CCI, lymphocyte counts, and S/Co ratio> 1.5, only S/Co ratio were independently associated with MICU admission and longer length of stay in hospital. This study recommends that titers of IgG targeting N-protein of SARS-CoV2 at admission is a prognostic factor for the clinical course of disease and should be measured in all patients with SARS-CoV2 infection.
Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models
Authors: Argawu Alemayehu Siffir 
Published: September 2020

AbstractBackgroundCOVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Côte d’Ivoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries.MethodsIn this study, the COVID 19 new cases data have been modeled and forecasted using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020.ResultsFrom July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Cote d□Ivoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote d□Ivoire and Ghana. The Algeria data was followed the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Cote d’Ivoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. The Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constantan for Cameroon, Cote d’Ivoire, Ghana and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020.ConclusionThis study was conducted with the current measures; the forecasts and trends nobtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their airspaces, and the countries’ other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.
Machine learning approaches classify clinical malaria outcomes based on haematological parameters
Authors: Morang’a Collins M. , Amenga–Etego Lucas , Bah Saikou Y. , Appiah Vincent , Amuzu Dominic S. , Amoako Nicholas , Abugri James , Oduro Abraham R. , Cunnington Aubrey J. , Awandare Gordon A. , Otto Thomas D. 
Published: September 2020

AbstractBackgroundMalaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI) remains a challenge. Furthermore, the success of rapid diagnostic tests (RDT) is threatened byPfhrp2/3deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM and severe malaria (SM) using haematological parameters.MethodsWe obtained haematological data from 2,207 participants collected in Ghana; nMI (n=978), UM (n=526), and SM (n=703). Six different machine learning approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agonistic explanations (LIME) were used to explain the binary classifiers.ResultsThe multi-classification model had greater than 85 % training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1-score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.960 (AUC= 0.983, and F1-score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location.ConclusionsThe study provides proof of concept methods that classify UM and SM from nMI, showing that ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness, and monitoring response to acute SM treatment particularly in endemic settings.
The COVID-19 PHARMACOME: A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization
Authors: Schultz Bruce , Zaliani Andrea , Ebeling Christian , Reinshagen Jeanette , Bojkova Denisa , Lage-Rupprecht Vanessa , Karki Reagon , Lukassen Sören , Gadiya Yojana , Ravindra Neal G. , Das Sayoni , Baksi Shounak , Domingo-Fernández Daniel , Lentzen Manuel , Strivens Mark , Raschka Tamara , Cinatl Jindrich , DeLong Lauren Nicole , Gribbon Phil , Geisslinger Gerd , Ciesek Sandra , van Dijk David , Gardner Steve , Kodamullil Alpha Tom , Fröhlich Holger , Peitsch Manuel , Jacobs Marc , Hoeng Julia , Eils Roland , Claussen Carsten , Hofmann-Apitius Martin 
Published: September 2020

AbstractThe SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing
Authors: Aevermann Brian , Zhang Yun , Novotny Mark , Bakken Trygve , Miller Jeremy , Hodge Rebecca , Lelieveldt Boudewijn , Lein Ed , Scheuermann Richard H. 
Published: September 2020

AbstractSingle cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell-types by surface protein expression to defining diseases by molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.
HIF1α-dependent induction of the mitochondrial chaperone TRAP1 regulates bioenergetic adaptations to hypoxia
Authors: Laquatra Claudio , Sanchez-Martin Carlos , Minervini Giovanni , Moroni Elisabetta , Schiavone Marco , Tosatto Silvio , Argenton Francesco , Colombo Giorgio , Bernardi Paolo , Masgras Ionica , Rasola Andrea 
Published: September 2020

AbstractThe mitochondrial paralog of the Hsp90 chaperone family TRAP1 is often induced in tumors, but the mechanisms controlling its expression, as well as its physiological functions remain poorly understood. Here, we find that TRAP1 is highly expressed in the early stages of Zebrafish development, and its ablation delays embryogenesis while increasing mitochondrial respiration of fish larvae. TRAP1 expression is enhanced by hypoxic conditions both in developing embryos and in cancer models of Zebrafish and mammals. The TRAP1 promoter contains evolutionary conserved hypoxic responsive elements, and HIF1α stabilization increases TRAP1 levels. TRAP1 inhibition by selective compounds or by genetic knock-out maintains a high level of respiration in Zebrafish embryos after exposure to hypoxia.Our data identify TRAP1 as a primary regulator of mitochondrial bioenergetics in highly proliferating cells following reductions in oxygen tension and HIF1α stabilization.
Prevalence of genetically similarFlavobacterium columnarephages across aquaculture environments reveals a strong potential for pathogen control
Authors: Runtuvuori-Salmela A , Kunttu HMT , Laanto E , Almeida GMF , Mäkelä K , Middelboe M , Sundberg L-R 
Published: September 2020

SummaryIntensive aquaculture conditions expose fish to bacterial infections, leading to significant financial losses, extensive antibiotic use and risk of antibiotic resistance in target bacteria.Flavobacterium columnarecauses columnaris disease in aquaculture worldwide. To develop a bacteriophage-based control of columnaris disease, we isolated and characterized 126F. columnarestrains and 63 phages againstF. columnarefrom Finland and Sweden. Bacterial isolates were virulent on rainbow trout (Oncorhynchus mykiss) and fell into four previously described genetic groups A, C, E and G, with genetic groups C and E being the most virulent. Phage host range studied against a collection of 228 bacterial isolates demonstrated modular infection patterns based on host genetic group. Phages infected contemporary and previously isolated bacterial hosts, but bacteria isolated most recently were generally resistant to previously isolated phages. Despite large differences in geographical origin, isolation year or host range of the phages, whole genome sequencing of 56 phages showed high level of genetic similarity to previously isolatedF. columnarephages (Ficleduovirus, Myoviridae). Altogether, this phage collection demonstrates a potential to be used in phage therapy.Significance StatementBacteriophages were discovered already over a century ago, and used widely in treatment of bacterial diseases before the era of antibiotics. Due to harmful effects of antibiotic leakage into environment, aquaculture is a potential target for phage therapy. However, the development of efficient phage therapy approach requires detailed characterization of bacterial pathogen virulence and phage host range. Here, we describe phage-bacterium interactions in the fish pathogenFlavobacterium columnare. We found that genetically similar phages are found from different fish farms, and their infectivity cluster according to genetic group of bacteria. In addition, phages were able to infect bacterial hosts from other farms, which is a preferable trait considering phage therapy approach. However, the most recently isolated phages had broader host range than the previously isolated phages, suggesting a response in the phage community to evolution of resistance in the bacteria. These results show that designing phage therapy for aquaculture (and other) systems needs consideration of both temporal and geographical aspects of the phage-bacterium interaction.
A phospho-regulated signal motif determines subcellular localization of α-TAT1 for dynamic microtubule acetylation
Authors: Roy Abhijit Deb , Gross Evan G. , Pillai Gayatri S. , Seetharaman Shailaja , Etienne-Manneville Sandrine , Inoue Takanari 
Published: September 2020

AbstractSpatiotemporally dynamic microtubule acetylation underlies diverse physiological events ranging from cell migration to intracellular trafficking, autophagy and viral infections. Despite its ubiquity, the molecular mechanisms that regulate the sole microtubule acetylating agent, α-tubulin N-acetyltransferase 1 (α-TAT1) remain obscure. Here we report that dynamic intracellular localization of α-TAT1 unexpectedly determines the efficiency of microtubule acetylation. Specifically, we newly identified a conserved signal motif in the intrinsically disordered C-terminus of α-TAT1, consisting of three competing regulatory elements - nuclear export, nuclear import and cytosolic retention. Their balance is tuned via phosphorylation by serine-threonine kinases including CDK1 and CK2. While the un-phosphorylated form resides both in the cytosol and nucleus, the phosphorylated form binds to specific 14-3-3 adapters and accumulates in the cytosol for maximal substrate access. Cytosolic localization of α-TAT1 predominantly mediates microtubule acetylation, cell proliferation and DNA damage response. In contrast to other molecules with a similar phospho-regulated signal motif including transcription factors, α-TAT1 uniquely uses the nucleus as a hideout. As amino acid mutations to the motif have been reported in cancer patients, the present mechanism of subcellular α-TAT1 localization may help uncover a spatiotemporal code of microtubule acetylation in normal and aberrant cell functions.