bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonthermal plasma treatment of polymers modulates biological fouling but can cause material embrittlement
Authors: Learn Greg D. , Lai Emerson J. , Wilson Emily J. , von Recum Horst A. 
Published: November 2019

AbstractPlasma-based treatment is a prevalent strategy to alter biological response and enhance biomaterial coating quality at the surfaces of biomedical devices and implants, especially polymeric materials. Plasma, an ionized gas, is often thought to have negligible effects on the bulk properties of prosthetic substrates given that it alters the surface chemistry on only the outermost few nanometers of material. However, no studies to date have systematically explored the effects of plasma exposure on both the surface and bulk properties of a biomaterial. This work examines the time-dependent effects of a nonthermal plasma on the surface and bulk properties of polymeric implants, specifically polypropylene surgical meshes and sutures. Findings suggest that plasma exposure improved resistance to fibrinogen adsorption and Escherichia coli attachment, and promoted mammalian fibroblast attachment, although increased duration of exposure resulted in a state of diminishing returns. At the same time, it was observed that plasma exposure can be detrimental to the material properties of individual filaments (i.e. sutures), as well as the structural characteristics of knitted meshes, with longer exposures resulting in further embrittlement and larger changes in anisotropic qualities. Though there are few guidelines regarding appropriate mechanical properties of surgical textiles, the results from this investigation imply that there are ultimate exposure limits for plasma-based treatments of polymeric implant materials when structural properties must be preserved, and that the effects of a plasma on a given biomaterial should be examined carefully before translation to a clinical scenario.
Prediction of liquid-liquid phase separation proteins using machine learning
Authors: Sun Tanlin , Li Qian , Xu Youjun , Zhang Zhuqing , Lai Luhua , Pei Jianfeng 
Published: November 2019

AbstractThe liquid-liquid phase separation (LLPS) of bio-molecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular functions. The dysregulation of LLPS might be implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. Here, a sequence-based prediction tool using machine learning for LLPS proteins (PSPredictor) was developed. Our model can achieve a maximum 10-CV accuracy of 96.03%, and performs much better in identifying new PSPs than reported PSP prediction tools. As far as we know, this is the first attempt to make a direct and more general prediction on LLPS proteins only based on sequence information.
An allosteric hot spot in the tandem-SH2 domain of ZAP-70 regulates T-cell signaling
Authors: Gangopadhyay Kaustav , Manna Bharat , Roy Swarnendu , Kumari Sunitha , Debnath Olivia , Chowdhury Subhankar , Ghosh Amit , Das Rahul 
Published: November 2019

AbstractT-cell receptor (TCR) signaling is initiated by recruiting ZAP-70 to the cytosolic part of TCR. ZAP-70, a non-receptor tyrosine kinase, is composed of an N-terminal tandem SH2 (tSH2) domain connected to the C-terminal kinase domain. The ZAP-70 is recruited to the membrane through binding of tSH2 domain and the doubly-phosphorylated ITAM motifs of CD3 chains in the TCR complex. Our results show that the tSH2 domain undergoes a biphasic structural transition while binding to the doubly-phosphorylated ITAM-ζ1 peptide. The C-terminal SH2 domain binds first to the phosphotyrosine residue of ITAM peptide to form an encounter complex leading to subsequent binding of second phosphotyrosine residue to the N-SH2 domain. We decipher a network of non-covalent interactions that allosterically couple the two SH2 domains during binding to doubly-phosphorylated ITAMs. Mutation in the allosteric network residues, for example, W165C, uncouples the formation of encounter complex to the subsequent ITAM binding thus explaining the altered recruitment of ZAP-70 to the plasma membrane causing autoimmune arthritis in mice. The proposed mechanism of allosteric coupling is unique to ZAP-70, which is fundamentally different from Syk, a close homolog of ZAP-70 expressed in B-cells.SignificanceT-cell and B-cell signaling is initiated by the same family of non-receptor tyrosine kinases, ZAP-70 and Syk, respectively. ZAP-70 and Syk share homologous sequence and similar structural architecture, yet the two kinases differ in their mode of ligand recognition. ZAP-70 binds cooperatively to its ligand, whereas Syk binds uncooperatively. Spontaneous mutation (W165C) in the regulatory module of ZAP-70 impairs T-cell signaling causes autoimmune arthritis in SKG mice, the mechanism of which is unknown. We showed that ZAP-70 regulatory module undergoes a biphasic structural transition while binding to its ligand, which is fundamentally different from Syk. We presented a molecular mechanism of cooperativity in ZAP-70 regulatory module that explains altered ligand binding by ZAP-70 mutant found in SKG mice.
Metagenomics of the modern and historical human oral microbiome with phylogenetic studies onStreptococcus mutansandStreptococcus sobrinus
Authors: Achtman Mark , Zhou Zhemin 
Published: November 2019

AbstractWe have recently developed bioinformatic tools to accurately assign metagenomic sequence reads to microbial taxa: SPARSE [1] for probabilistic, taxonomic classification of sequence reads, EToKi [2] for assembling and polishing genomes from short read sequences, and GrapeTree [3], a graphic visualizer of genetic distances between large numbers of genomes. Together, these methods support comparative analyses of genomes from ancient skeletons and modern humans [2,4]. Here we illustrate these capabilities with 784 samples from historical dental calculus, modern saliva and modern dental plaque. The analyses revealed 1591 microbial species within the oral microbiome. We anticipated that the oral complexes of Socranskyet al.[5] would predominate among taxa whose frequencies differed by source. However, although some species discriminated between sources, we could not confirm the existence of the complexes. The results also illustrate further functionality of our pipelines with two species that are associated with dental caries,Streptococcus mutansandStreptococcus sobrinus.They were rare in historical dental calculus but common in modern plaque, and even more common in saliva. Reconstructed draft genomes of these two species from metagenomic samples in which they were abundant were combined with modern public genomes to provide a detailed overview of their core genomic diversity.
Evidence for shared ancestry between Actinobacteria and Firmicutes bacteriophages
Authors: Koert Matthew , López-Pérez Júlia , Mattson Courtney , Caruso Steven , Erill Ivan 
Published: November 2019

ABSTRACTBacteriophages typically infect a small set of related bacterial strains. The transfer of bacteriophages between more distant clades of bacteria has often been postulated, but remains mostly unaddressed. In this work we leverage the sequencing of a novel cluster of phages infecting Streptomyces bacteria and the availability of large numbers of complete phage genomes in public repositories to address this question. Using phylogenetic and comparative genomics methods, we show that several clusters of Actinobacteria-infecting phages are more closely related between them, and with a small group of Firmicutes phages, than with any other actinobacteriophage lineage. These data indicate that this heterogeneous group of phages shares a common ancestor with well-defined genome structure. Analysis of genomic %GC content and codon usage bias shows that these actinobacteriophages are poorly adapted to their Actinobacteria hosts, suggesting that this phage lineage could have originated in an ancestor of the Firmicutes, adapted to the low %GC content members of this phylum, and later migrated to the Actinobacteria, or that selective pressure for enhanced translational throughput is significantly lower for phages infecting Actinobacteria hosts.
MXD/MIZ1 complexes activate transcription of MYC-repressed genes
Authors: Shostak Anton , Schermann Géza , Diernfellner Axel , Brunner Michael 
Published: November 2019

ABSTRACTMXD proteins are transcription repressors that antagonize the E-box dependent activation of genes by MYC. MYC together with MIZ1 acts also as a repressor of a subset of genes, including cell cycle inhibitor genes such as p15 and p21. A role of MXDs in regulation of MYC-repressed genes is not known. Here we report that MXDs are functionally expressed in U2OS cells and activate transcription of p15 and p21, and other MYC-repressed genes. Activation of transcription was dependent on the interaction of MXDs with MIZ1, and on an intact DNA binding domain. MIZ1-binding deficient MXD mutants interacted with MAX and were active as repressors of MYC-activated genes but failed to activate MYC-repressed genes. Mutant MXDs with reduced DNA binding affinity interacted with MAX and MIZ1 but neither repressed nor activated transcription. Overexpression of MXDs attenuated proliferation of U2OS cells predominantly via MIZ1-dependent induction of p21. Our data show that MXDs and MYC have a reciprocally antagonistic potential to regulate transcription of mutual target genes.
Three-dimensional traction microscopy accounting for cell-induced matrix degradation
Authors: Song Dawei , Seidl D. Thomas , Oberai Assad A 
Published: November 2019

AbstractTractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morpho-genesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically accurate neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Our method can extend the range of applicability of 3DTM.
Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration
Authors: McGowan Thomas , Johnson James E. , Kumar Praveen , Sajulga Ray , Mehta Subina , Jagtap Pratik D. , Griffin Timothy J. 
Published: November 2019

AbstractBackgroundProteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate ‘omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation.FindingsMVP is built as an HTML Galaxy plugin, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input -- a custom datatype (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript (IGVjs) framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface.ConclusionsMVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.
A highly multiplexed melt-curve assay for detecting the most prevalent carbapenemase, ESBL and AmpC genes
Authors: Edwards T. , Williams C. , Teethaisong Y. , Sealey J. , Sasaki S. , Hobbs G. , Cuevas L.E. , Evans K. , Adams E.R. 
Published: November 2019

AbstractResistance to third generation cephalosporins and carbapenems in Gram-negative bacteria is chiefly mediated by beta-lactamases including ESBL, AmpC and carbapenemase enzymes. Routine phenotypic detection methods do not provide timely results, and there is a lack of comprehensive molecular panels covering all important markers.An ESBL/carbapenemase HRM assay (SHV, TEM, CTX-M ESBLs, and NDM, IMP, KPC, VIM and OXA-48-like carbapenemases) and an AmpC HRM assay (16S rDNA control, FOX, MOX, ACC, EBC, CIT and DHA) were designed, and evaluated on 111 Gram-negative isolates with mixed resistance patterns.The sensitivity for carbapenemase, ESBL and AmpC genes was 96.7% (95%CI:82.8-99.9%), 93.6% (95%CI:85.7-97.9%) and 93.8% (95%CI:82.8-98.7%), respectively with a specificity of 100% (95%CI:95.6-100%), 93.9% (95%CI:79.8-99.3%) and 93.7% (95%CI:84.5-98.2%).The HRM assays enable the simultaneous detection of the fourteen most important ESBL, carbapenemase and AmpC genes and could be used as a molecular surveillance tool or to hasten detection of AMR for treatment management.
Insulin signaling gates long-term memory formation in Drosophila larvae
Authors: Eschment Melanie , Franz Hanna R. , Güllü Nazlı , Hölscher Luis G. , Huh Ko-Eun , Widmann Annekathrin 
Published: November 2019

AbstractThe ability to learn new skills and to store them as memory entities is one of the most impressive features of higher evolved organisms. However, not all memories are created equal; some are short-lived forms, and some are longer lasting. Formation of the latter is energetically costly and by the reason of restricted availability of food or fluctuations in energy expanses, efficient metabolic homeostasis modulating different needs like survival, growth, reproduction, or investment in longer lasting memories is crucial. Whilst equipped with cellular and molecular pre-requisites for formation of a protein synthesis dependent long-term memory (LTM), its existence in the larval stage of Drosophila remains elusive. Considering it from the viewpoint that larval brain structures are completely rebuilt during metamorphosis, and that this process depends completely on accumulated energy stores formed during the larval stage, investing in LTM represents an unnecessary expenditure. However, as an alternative, Drosophila larvae are equipped with the capacity to form a protein synthesis independent so-called larval anaesthesia resistant memory (lARM), which is consolidated in terms of being insensitive to cold-shock treatments. Motivated by the fact that LTM formation causes an increase in energy uptake in Drosophila adults, we tested the idea of whether an energy surplus can induce the formation of LTM in the larval stage. Indeed, increasing the metabolic state by feeding Drosophila larvae the disaccharide sucrose directly before aversive olfactory conditioning led to the formation of a larval LTM (lLTM). Moreover, we show that the metabolic state acts as a binary switch between the formation of lARM and lLTM. Based on this finding, we determined that it is the insulin receptor (InR) expressed in the mushroom body Kenyon cells (MB KCs) that mediates this switch to favor the formation of lLTM under energy-rich circumstances and lARM under energy-poor circumstances.
Associations between aversive learning processes and transdiagnostic psychiatric symptoms revealed by large-scale phenotyping
Authors: Wise Toby , Dolan Raymond J 
Published: November 2019

AbstractBackgroundSymptom expression in a range of psychiatric conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. 400 subjects completed an online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty, and tested for associations between these variables and traditional psychiatric constructs alongside transdiagnostic dimensions. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. We show that state anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced learning from safety, and data-driven analysis indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning, and heightened uncertainty estimation, to symptoms of depression and social anxiety. Our findings implicate distinct aversive learning processes in the expression of psychiatric symptoms that transcend traditional diagnostic boundaries.
A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases
Authors: Kumar Praveen , Johnson James E. , Easterly Caleb , Mehta Subina , Sajulga Ray , Nunn Brook , Jagtap Pratik D. , Griffin Timothy J. 
Published: November 2019

AbstractMulti-omics approaches focused on mass-spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics. We demonstrate increased true positive PSM identifications using the sectioning method when compared to the traditional large database searching method, whereas it helped in reducing the false PSM identifications when compared to a previously described two-step method for reducing database size. The sectioning method for large sequence databases enables generation of an enriched protein sequence database and promotes increased sensitivity in identifying PSMs, while maintaining acceptable and manageable FDR. Furthermore, implementation in the Galaxy platform provides access to a usable and automated workflow for carrying out the method. Our results show the utility of this methodology for a wide-range of applications where genome-guided, large sequence databases are required for MS-based proteomics data analysis.
Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers
Authors: He Bing , Gao Peng , Ding Yang-Yang , Chen Chia-Hui , Chen Gregory , Chen Changya , Kim Hannah , Tasian Sarah K. , Hunger Stephen P. , Tan Kai 
Published: November 2019

AbstractInterpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here we developed a computational framework to identify risk noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as candidate risk mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation signatures can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.
RNA modifications detection by comparative Nanopore direct RNA sequencing
Authors: Leger Adrien , Amaral Paulo P. , Pandolfini Luca , Capitanchik Charlotte , Capraro Federica , Barbieri Isaia , Migliori Valentina , Luscombe Nicholas M. , Enright Anton J , Tzelepis Konstantinos , Ule Jernej , Fitzgerald Tomas , Birney Ewan , Leonardi Tommaso , Kouzarides Tony 
Published: November 2019

AbstractRNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. To date, over 150 naturally occurring PTMs have been identified, however the overwhelming majority of their functions remain elusive. In recent years, a small number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing (DRS) technology has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework to evaluate the presence of modifications in DRS data. To do so, we compare an RNA sample of interest against a non-modified control sample. Our strategy does not require a training set and allows the use of replicates to model biological variability. Here, we demonstrate the ability of Nanocompore to detect RNA modifications at single-molecule resolution in human polyA+ RNAs, as well as in targeted non-coding RNAs. Our results correlate well with orthogonal methods, confirm previous observations on the distribution of N6-methyladenosine sites and provide novel insights into the distribution of RNA modifications in the coding and non-coding transcriptomes. The latest version of Nanocompore can be obtained at https://github.com/tleonardi/nanocompore.