bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional genomic analyses highlights a shift inGpr17-regulated cellular processes in oligodendrocyte progenitor cells (OPC) and underlying myelin dysregulation in the aged forebrain
Authors: Rivera Andrea D. , Pieropan Francesca , De La Rocha Irene Chacon , Lecca Davide , Abbracchio Maria P. , Azim Kasum , Butt Arthur M 
Published: October 2020

SummaryBrain aging is characterised by a decline in neuronal function and associated cognitive deficits. There is increasing evidence that myelin disruption is an important factor that contributes to the age-related loss of brain plasticity and repair responses. In the brain, myelin is produced by oligodendrocytes, which are generated throughout life by oligodendrocyte progenitor cells (OPCs). Currently, a leading hypothesis points to aging as a major reason for the ultimate breakdown of remyelination in Multiple Sclerosis (MS). However, an incomplete understanding of the cellular and molecular processes underlying brain aging hinders the development of regenerative strategies. Here, our combined systems biology and neurobiological approach demonstrates that oligodendroglial and myelin genes are amongst the most altered in the aging mouse cortex. This was underscored by the identification of causal links between signaling pathways and their downstream transcriptional networks that define oligodendroglial disruption in aging. The results highlighted that the G-protein coupled receptor GPR17 is central to the disruption of OPC in aging and this was confirmed by genetic fate mapping and cellular analyses. Finally, we used systems biology strategies to identify therapeutic agents that rejuvenate OPC and restore myelination in age-related neuropathological contexts.
Machine learning models for predicting protein condensate formation from sequence determinants and embeddings
Authors: Saar Kadi L. , Morgunov Alexey S. , Qi Runzhang , Arter William E. , Krainer Georg , Lee Alpha A. , Knowles Tuomas P. J. 
Published: October 2020

AbstractIntracellular phase separation of proteins into biomolecular condensates is increasingly recognised as an important phenomenon for cellular compartmentalisation and regulation of biological function. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, here, we established anin silicostrategy for understanding on a global level the associations between protein sequence and condensate formation, and used this information to construct machine learning classifiers for predicting liquid–liquid phase separation (LLPS) from protein sequence. Our analysis highlighted that LLPS–prone sequences are more disordered, hydrophobic and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database, and have their disordered regions enriched in polar, aromatic and charged residues. Using these determining features together with neural network based word2vec sequence embeddings, we developed machine learning classifiers for predicting protein condensate formation. Our model, trained to distinguish LLPS-prone sequences from structured proteins, achieved high accuracy (93%; 25-fold cross-validation) and identified condensate forming sequences from external independent test data at 97% sensitivity. Moreover, in combination with a classifier that had developed a nuanced insight into the features governing protein phase behaviour by learning to distinguish between sequences of varying LLPS propensity, the sensitivity was supplemented with high specificity (approximated ROC–AUC of 0.85). These results provide a platform rooted in molecular principles for understanding protein phase behaviour. The predictor is accessible fromhttps://deephase.ch.cam.ac.uk/.Significance StatementThe tendency of many cellular proteins to form protein-rich biomolecular condensates underlies the formation of subcellular compartments and has been linked to various physiological functions. Understanding the molecular basis of this fundamental process and predicting protein phase behaviour have therefore become important objectives. To develop a global understanding of how protein sequence determines its phase behaviour, here, we constructed bespoke datasets of proteins of varying phase separation propensity and identified explicit biophysical and sequence-specific features common to phase separating proteins. Moreover, by combining this insight with neural network based sequence embeddings, we trained machine learning classifiers that identified phase separating sequences with high accuracy, including from independent external test data. The predictor is available fromhttps://deephase.ch.cam.ac.uk/.
A Single-Cell Tumor Immune Atlas for Precision Oncology
Authors: Nieto Paula , Elosua-Bayes Marc , Trincado Juan L. , Marchese Domenica , Massoni-Badosa Ramon , Salvany Maria , Henriques Ana , Mereu Elisabetta , Moutinho Catia , Ruiz Sara , Lorden Patricia , Chin Vanessa T. , Kaczorowski Dominik , Chan Chia-Ling , Gallagher Richard , Chou Angela , Planas-Rigol Ester , Rubio-Perez Carlota , Gut Ivo , Piulats Josep M. , Seoane Joan , Powell Joseph E. , Batlle Eduard , Heyn Holger 
Published: October 2020

AbstractThe tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enablein situmapping of immune populations for digital pathology, we appliedSPOTlight, combining single-cell and spatial transcriptomics data and identifying striking spatial immune cell patterns in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immuno-therapy.
Downregulation of miR-17-92 cluster by PERK fine-tunes unfolded protein response mediated apoptosis
Authors: Read Danielle E. , Gupta Ananya , Cawley Karen , Fontana Laura , Agostinis Patrizia , Samali Afshin , Gupta Sanjeev 
Published: October 2020

AbstractAn important event in the unfolded protein response (UPR) is the activation of the endoplasmic reticulum kinase PERK (EIF2AK3). The PERK signalling branch first mediates a prosurvival response, which switches into a proapoptotic response upon prolonged ER stress. However, the molecular mechanisms of PERK-mediated cell death are not well understood. Here we show that expression of the primary miR-17-92 transcript and mature miRNAs belonging to miR-17-92 cluster is decreased during UPR. We found that activity of miR-17-92 promoter reporter was reduced during UPR in a PERK-dependent manner. We show that activity of miR-17-92 promoter is repressed by ectopic expression of ATF4 and NRF2. The promoter deletion analysis and ChIP assays mapped the region responding to UPR-mediated repression to site in the proximal region of the miR-17-92 promoter. Hypericin-mediated photo-oxidative ER damage reduced the expression of miRNAs belonging to miR-17-92 cluster in wild-type but not in PERK-deficient cells. Importantly, ER stress-induced apoptosis was inhibited upon miR-17-92 overexpression in SH-SY5Y and H9c2 cells. Our results reveal a novel function for NRF2, where repression of miR-17-92 cluster by NRF2 plays an important role in ER stress-mediated apoptosis. The data presented here provides mechanistic details how sustained PERK signalling via NRF2 mediated repression of miR-17-92 cluster can potentiate cell death.
Origin of imported SARS-CoV-2 strains in The Gambia identified from whole genome sequences
Authors: Kanteh Abdoulie , Manneh Jarra , Jabang Sona , Kujabi Mariama A. , Sanyang Bakary , Oboh Mary A. , Bojang Abdoulie , Jallow Haruna S. , Nwakanma Davis , Secka Ousman , Roca Anna , Amambua-Ngwa Alfred , Antonio Martin , Baldeh Ignatius , Forrest Karen , Samateh Ahmadou Lamin , D’Alessandro Umberto , Sesay Abdul Karim 
Published: October 2020

AbstractSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-sense single stranded RNA virus with high human transmissibility. This study generated Whole Genome data to determine the origin and pattern of transmission of SARS-CoV-2 from the first six cases tested in The Gambia. Total RNA from SARS-CoV-2 was extracted from inactivated nasopharyngeal-oropharyngeal swabs of six cases and converted to cDNA following the ARTIC COVID-19 sequencing protocol. Libraries were constructed with the NEBNext ultra II DNA library prep kit for Illumina and Oxford Nanopore Ligation sequencing kit and sequenced on Illumina MiSeq and Nanopore GridION, respectively. Sequencing reads were mapped to the Wuhan reference genome and compared to eleven other SARS-CoV-2 strains of Asian, European and American origins. A phylogenetic tree was constructed with the consensus genomes for local and non-African strains. Three of the Gambian strains had a European origin (UK and Spain), two strains were of Asian origin (Japan). In The Gambia, Nanopore and Illumina sequencers were successfully used to identify the sources of SARS-CoV-2 infection in COVID-19 cases.
The Fc-mediated effector functions of a potent SARS-CoV-2 neutralizing antibody, SC31, isolated from an early convalescent COVID-19 patient, are essential for the optimal therapeutic efficacy of the antibody
Authors: Chan Conrad E.Z. , Seah Shirley G.K. , Chye De Hoe , Massey Shane , Torres Maricela , Lim Angeline P.C. , Wong Steven K.K. , Neo Jacklyn J.Y. , Wong Pui San , Lim Jie Hui , Loh Gary S.L. , Wang Dong Ling , Boyd-Kirkup Jerome D. , Guan Siyu , Thakkar Dipti , Teo Guo Hui , Purushotorman Kiren , Hutchinson Paul E. , Young Barnaby E. , Lye David C. , Low Jenny G. , MacAry Paul A. , Hentze Hannes , Prativadibhayankara Venkateshan S. , Ethirajulu Kantharaj , O’Connell Damian , Comer Jason , Tseng Chien-Te K. , Barrett Alan D.T. , Ingram Piers J. , Brasel Trevor , Hanson Brendon J. 
Published: October 2020

AbstractSARS-CoV-2-neutralizing antibodies are promising therapeutics for COVID-19. However, little is known about the mechanisms of action of these antibodies or their effective dosing windows. We report the discovery and development of SC31, a potent SARS-CoV-2 neutralizing IgG1 antibody, originally isolated from a convalescent patient at day 27 after the onset of symptoms. Neutralization occurs via a binding epitope that maps within the ACE2 interface of the SARS-CoV-2 Spike protein, conserved across all common circulating SARS-CoV-2 mutants. In SARS-CoV-2 infected K18-human ACE2 transgenic mice, SC31 demonstrated potent survival benefit by dramatically reducing viral load concomitant with attenuated pro-inflammatory responses linked to severe systemic disease, such as IL-6. Comparison with a Fc-null LALA variant of SC31 demonstrated that optimal therapeutic efficacy of SC31 requires intact Fc-mediated effector functions that can further induce an IFNγ-driven anti-viral immune response. Dose-dependent efficacy for SC31 was observed down to 5mg/kg when dosed before the activation of lung inflammatory responses. Importantly, despite FcγR binding, no evidence of antibody dependent enhancement was observed with the Fc-competent SC31 even at sub-therapeutic doses. Therapeutic efficacy was confirmed in SARS-CoV-2-infected hamsters, where SC31 again significantly reduced viral load, decreased lung lesions and inhibited progression to severe disease manifestations. This study underlines the potential for significant COVID-19 patient benefit for the SC31 antibody that justifies rapid advancement to the clinic, as well as highlighting the importance of appropriate mechanistic and functional studies during development.One Sentence SummaryAnti-SARS-CoV-2 IgG1 antibody SC31 controls infectionin vivoby blocking SP:ACE2 binding and triggering a Fc-mediated anti-viral response.
A Biological Inventory of Prophages inA. baumanniiGenomes Reveal Distinct Distributions in Classes, Length and Genomic Positions
Authors: Loh Belinda , Chen Jiayuan , Manohar Prasanth , Yu Yunsong , Hua Xiaoting , Leptihn Sebastian 
Published: October 2020

AbstractAcinetobacter baumanniiis of major clinical importance as the bacterial pathogen often causes hospital acquired infections, further complicated by the high prevalence of antibiotic resistant strains. Aside from natural tolerance to certain antibiotic classes, resistance is often acquired by the exchange of genetic information via conjugation but also by the high natural competence exhibited byA. baumannii. In addition, bacteriophages are able to introduce resistance genes but also toxins and virulence factors via phage mediated transduction. In this work, we analysed the complete genomes of 177A. baumanniistrains for the occurrence of prophages, and analysed their taxonomy, size and positions of insertion. Among all the prophages that were detected, Siphoviridae and Myoviridae were the two most commonly found families, while the average genome size was determined as 3.98 Mbp. Our data shows the wide variation in the number of prophages inA. baumanniigenomes and the prevalence of certain prophages within strains that are most “successful” or potentially beneficial to the host. Our study also revealed that only two specific sites of insertion within the genome of the host bacterium are being used, with few exceptions only. Lastly, we analysed the existence of genes that are encoded in the prophages, which confer antimicrobial resistance (AMR). Several phages carry AMR genes, including OXA-23 and NDM-1, illustrating the importance of lysogenic phages in the acquisition of resistance genes.
Three-dimensional biofilm growth supports a mutualism involving matrix and nutrient sharing
Authors: Arjes Heidi A. , Willis Lisa , Gui Haiwen , Xiao Yangbo , Peters Jason , Gross Carol , Huang Kerwyn Casey 
Published: October 2020

SummaryLife in a three-dimensional structure such as a biofilm is typical for many bacteria, yet little is known about how strains with different genotypes interact in this context. Here, to systematically explore gene knockdowns across various three-dimensional contexts, we created arrayed libraries of essential-gene CRISPRi knockdowns in the model biofilm-forming bacteriumBacillus subtilisand measured competitive fitness during colony co-culture with a wild type-like parent on different media and at different knockdown levels. Partial knockdown led to a wide range of fitness phenotypes, with targeting of translation-related genes often leading to lower growth rates and rapid out-competition by the parent. Several knockdowns competed differentially in biofilms versus non-biofilm colonies, in some cases due to lack of a particular nutrient in one medium. Cells depleted for the alanine racemase AlrA died in monoculture, but co-cultures survived via nutrient sharing in a biofilm but not in liquid. This rescue was enhanced in biofilm co-culture with a parent unable to produce extracellular matrix, due to a mutualism involving nutrient and matrix sharing. IncludingalrA, we identified several examples of mutualism involving matrix sharing that occurred in a three-dimensional biofilm colony but not when growth was constrained to two dimensions. These findings demonstrate that growth in a three-dimensional biofilm can promote genetic diversity through sharing of secreted factors, and illustrate the role of matrix production in determining trajectories for biofilm evolution that may be relevant to pathogens and other environmental bacteria.
Principal component analysis of RNA-seq data unveils a novel prostate cancer-associated gene expression signature
Authors: Perera Yasser , Gonzalez Augusto , Perez Rolando 
Published: October 2020

AbstractProstate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1), validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating driver’s genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis comprising major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures may describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.
Codon Usage Bias Levels Predict Taxonomic Identity and Genetic Composition
Authors: Khomtchouk Bohdan B. 
Published: October 2020

AbstractIn this study, we investigate how an organism’s codon usage bias levels can serve as a predictor and classifier of various genomic and evolutionary features across the three kingdoms of life (archaea, bacteria, eukarya). We perform secondary analysis of existing genetic datasets to build several artificial intelligence (AI) and machine learning models trained on over 13,000 organisms that show it is possible to accurately predict an organism’s DNA type (nuclear, mitochondrial, chloroplast) and taxonomic identity simply using its genetic code (64 codon usage frequencies). By leveraging advanced AI and machine learning methods to accurately identify evolutionary origins and genetic composition from codon usage patterns, our study suggests that the genetic code can be utilized to train accurate machine learning classifiers of taxonomic and phylogenetic features. Our dataset and analyses are made publicly available on Github and the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Codon+usage) to facilitate open-source reproducibility and community engagement.
A scalable saliva-based, extraction-free rt-lamp protocol for sars-cov-2 diagnosis
Authors: Asprino Paula , Bettoni Fabiana , Camargo Anamaria , Coelho Diego , Coppini Guilherme , Correa Igor , Freitas Erika , Inoue Lilian , Kitajima João Paulo , Kuroki Mayra , Masotti Cibele , Marques Tatiana , Reis Alice , Reis Luiz Fernando , Santos Bibiana , Santos Ernande dos , Schlesinger David , Sena Cecília , Spadaccini Talita , Taniguti Lucas 
Published: October 2020

I.ABSTRACTScalable, cost-effective screening methods are an essential tool to control SARS-CoV-2 spread. We have developed a straight saliva-based, RNA extraction-free, RT-LAMP test that is comparable to current nasopharyngeal swab RT-PCR tests in both sensitivity and specificity. Using a 2-step readout of fluorescence and melting-point curve analysis, the test is scalable to more than 30,000 tests per day with average turnaround time of less than 3 hours. The test was validated using samples from 244 symptomatic patients, and showed sensitivity of 78.9% (vs. 85.5% for nasopharyngeal swabs RT-PCR) and specificity of 100% (vs. 100% for nasopharyngeal swabs RT-PCR). Our method is therefore accurate, robust, time and cost effective and therefore can be used for screening of SARS-CoV-2.
Logics, complexities and paradoxical tensions: management controls in a clustered firm
Authors: Damayanthi Sujeewa , Gooneratne Tharusha N. , Jayakody J.A.S.K. 
Published: October 2020

PurposeThis paper explores how management controls of a clustered apparel firm in Sri Lanka (Stitch-It) is shaped by institutional field and societal logics, firm's head office prescriptions, clusters' own attributes and strategic behavior of cluster managers.Design/methodology/approachIt follows the research philosophy of interpretivism and embedded case study approach within the qualitative research design, while institutional complexity within the institutional logics perspective and paradoxical tensions, organizational attributes and strategic responses to institutional processes provide the theoretical underpinning.FindingsThe findings suggest that market, profession and state logics in the apparel field, alongside community logic at the societal level, develop a state of complexity in Stitch-It and its clusters. At the cluster level, such complexity is further intensified by head office guidelines (on controls), which gets filtered by the organizational attributes of the particular clusters. At this state, paradoxical tensions are developed within clusters, and to mitigate such tensions, key organizational members employ different strategies, which in turn shape management controls of the clusters.Practical implicationsThis paper highlights that practicing managers need to be mindful of different logics in the field, organizational attributes, resulting tensions, complexities, strategies to deal with them and their ramifications on controls.Originality/valueThe paper asserts that management controls is a dynamic and a situational phenomenon, which continuously evolves in light of organizational attributes, multiple logics and head office prescriptions. It conceptualizes the “tensions” evident in the design and implementation of management controls, arising due to multiplicity of pressures as “paradoxical tensions.” Although important and relevant to management control arena, “paradoxical tensions” has been scantly explored by prior researchers.