FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry

Background: Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.Results: The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.Conclusions: The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. © 2012 Lun et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Testing for differential abundance in mass cytometry data.

When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.This work was supported by Cancer Research UK (core funding to J.C.M., award no. A17197), the University of Cambridge and Hutchison Whampoa Limited. J.C.M. was also supported by core funding from EMBL

COMRADES determines in vivo RNA structures and interactions.

The structural flexibility of RNA underlies fundamental biological processes, but there are no methods for exploring the multiple conformations adopted by RNAs in vivo. We developed cross-linking of matched RNAs and deep sequencing (COMRADES) for in-depth RNA conformation capture, and a pipeline for the retrieval of RNA structural ensembles. Using COMRADES, we determined the architecture of the Zika virus RNA genome inside cells, and identified multiple site-specific interactions with human noncoding RNAs.This work was supported by Cancer Research UK (C13474/A18583, C6946/A14492) and the Wellcome Trust (104640/Z/14/Z, 092096/Z/10/Z) to E.A.M. O.Z. was supported by the Human Frontier Science Program (HFSP, LT000558/2015), the European Molecular Biology Organization (EMBO, ALTF1622-2014), and the Blavatnik Family Foundation postdoctoral fellowship. G.K. and M.G. were supported by Wellcome Trust grant 207507 and UK Medical Research Council. A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. 17197 to JCM). J.C.M was also supported by core funding from EMBL. I.G. and L.W.M. were supported by the Wellcome Trust Senior Fellowship in Basic Biomedical Science to I.G. (207498/Z/17/Z). I.J.M., L.F.G. and J.S.-G. were supported by grants R01GM104475 and R01GM115649 from NIGMS. C.K.K was supported by City University of Hong Kong Projects 9610363 and 7200520, Croucher Foundation Project 9500030 and Hong Kong RGC Projects 9048103 and 9054020. C.-F.Q. was supported by the NSFC Excellent Young Scientist Fund 81522025 and the Newton Advanced Fellowship from the Academy of Medical Sciences, UK

T cell cytolytic capacity is independent of initial stimulation strength.

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell-activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses

Unlocking the Transcriptomes of Two Carcinogenic Parasites, Clonorchis sinensis and Opisthorchis viverrini

The two parasitic trematodes, Clonorchis sinensis and Opisthorchis viverrini, have a major impact on the health of tens of millions of humans throughout Asia. The greatest impact is through the malignant cancer ( = cholangiocarcinoma) that these parasites induce in chronically infected people. Therefore, both C. sinensis and O. viverrini have been classified by the World Health Organization (WHO) as Group 1 carcinogens. Despite their impact, little is known about these parasites and their interplay with the host at the molecular level. Recent advances in genomics and bioinformatics provide unique opportunities to gain improved insights into the biology of parasites as well as their relationships with their hosts at the molecular level. The present study elucidates the transcriptomes of C. sinensis and O. viverrini using a platform based on next-generation (high throughput) sequencing and advanced in silico analyses. From 500,000 sequences, >50,000 sequences were assembled for each species and categorized as biologically relevant based on homology searches, gene ontology and/or pathway mapping. The results of the present study could assist in defining molecules that are essential for the development, reproduction and survival of liver flukes and/or that are linked to the development of cholangiocarcinoma. This study also lays a foundation for future genomic and proteomic research of C. sinensis and O. viverrini and the cancers that they are known to induce, as well as novel intervention strategies

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data [version 1; referees: 2 approved, 1 approved with reservations]

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data [version 2; referees: 2 approved, 1 approved with reservations]

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies