scholarly journals Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments

2016 ◽  
Author(s):  
Samuel L. Díaz Muñoz
Keyword(s):  
Single Cell ◽  
Strong Predictor ◽  
Viral Evolution ◽  
Data Sets ◽  
Host Interactions ◽  
Two Factors ◽  
Comprehensive Picture ◽  
Host Ecology ◽  
Viral Coinfection ◽  
Virus Interactions

AbstractInfection of more than one virus in a host, coinfection, is common across taxa and environments. Viral coinfection can enable genetic exchange, alter the dynamics of infections, and change the course of viral evolution. Yet, a systematic test of the factors explaining variation in viral coinfection across different taxa and environments awaits completion. Here I employ three microbial data sets of virus-host interactions covering cross-infectivity, culture coinfection, and single-cell coinfection (total: 6,564 microbial hosts, 13,103 viruses) to provide a broad, comprehensive picture of the ecological and biological factors shaping viral coinfection. I found evidence that ecology and virus-virus interactions are recurrent factors shaping coinfection patterns. Host ecology was a consistent and strong predictor of coinfection across all three datasets: cross-infectivity, culture coinfection, and single-cell coinfection. Host phylogeny or taxonomy was a less consistent predictor, being weak or absent in the cross-infectivity and single-cell coinfection models, yet it was the strongest predictor in the culture coinfection model. Virus-virus interactions strongly affected coinfection. In the largest test of superinfection exclusion to date, prophage sequences reduced culture coinfection by other prophages, with a weaker effect on extrachromosomal virus coinfection. At the single-cell level, prophage sequences eliminated coinfection. Virus-virus interactions alsoincreasedculture coinfection with ssDNA-dsDNA coinfections >2x more likely than ssDNA-only coinfections. The presence of CRISPR spacers was associated with a ~50% reduction in single-cell coinfection in a marine bacteria, despite the absence of exact spacer matches in any active infection. Collectively, these results suggest the environment bacteria inhabit and the interactions among surrounding viruses are two factors consistently shaping viral coinfection patterns. These findings highlight the role of virus-virus interactions in coinfection with implications for phage therapy, microbiome dynamics, and viral infection treatments.

scholarly journals Virus-host coexistence in phytoplankton through the genomic lens

2019 ◽  
Author(s):  
Yau Sheree ◽  
Marc Krasovec ◽  
Stephane Rombauts ◽  
Mathieu Groussin ◽  
L. Felipe Benites ◽  
...  
Keyword(s):  
Single Cell ◽  
Orthologous Genes ◽  
Hedging Strategy ◽  
Continuous Growth ◽  
Phase Switching ◽  
Host Interactions ◽  
Boom And Bust ◽  
Nw Mediterranean ◽  
Nw Mediterranean Sea ◽  
Virus Interactions

AbstractPhytoplankton-virus interactions are major determinants of geochemical cycles in the oceans. Viruses are responsible for the redirection of carbon and nutrients away from larger organisms back towards microorganisms via the lysis of microalgae in a process coined the ‘viral shunt’. Virus-host interactions are generally expected to follow ‘boom and bust’ dynamics, whereby a numerically dominant strain is lysed and replaced by a virus resistant strain. Here, we isolated a microalga and its infective nucleo-cytoplasmic large DNA virus (NCLDV) concomitantly from the environment in the surface NW Mediterranean Sea, Ostreococcus mediterraneus, and show continuous growth in culture of both the microalga and the virus. Evolution experiments through single cell bottlenecks demonstrate that, in the absence of the virus, susceptible cells evolve from one ancestral resistant single cell, and vice–versa; that is that resistant cells evolve from one ancestral susceptible cell. This provides evidence that the observed sustained viral production is the consequence of a minority of virus-susceptible cells. The emergence of these cells is explained by low-level phase switching between virus-resistant and virus-susceptible phenotypes, akin to a bet hedging strategy. Whole genome sequencing and analysis of the ~14 Mb microalga and the ~200 kb virus points towards ancient speciation of the microalga within the Ostreococcus species complex and frequent gene exchanges between prasinoviruses infecting Ostreococcus species. Re-sequencing of one susceptible strain demonstrated that the phase switch involved a large 60 Kb deletion of one chromosome. This chromosome is an outlier chromosome compared to the streamlined, gene dense, GC-rich standard chromosomes, as it contains many repeats and few orthologous genes. While this chromosome has been described in three different genera, its size increments have been previously associated to antiviral immunity and resistance in another species from the same genus. Mathematical modelling of this mechanism predicts microalga–virus population dynamics consistent with the observation of continuous growth of both virus and microalga. Altogether, our results suggest a previously overlooked strategy in phytoplankton–virus interactions.

scholarly journals Uncovering Virus-Virus Interactions by Unifying Approaches and Harnessing High-Throughput Tools

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Samuel L. Díaz-Muñoz
Keyword(s):  
Social Interactions ◽  
High Throughput ◽  
Bacterial Infections ◽  
Social Evolution ◽  
Viral Evolution ◽  
Genetic Composition ◽  
Host Interactions ◽  
Basic Concepts ◽  
New Treatments ◽  
Virus Interactions

ABSTRACTVirus-host interactions have received much attention in virology. Virus-virus interactions can occur when >1 virus infects a host and can be deemed social when one virus affects the fitness of another virus, as in the well-known case of superinfection exclusion. Coinfection and subsequent social interactions can change viral pathogenicity, host range, and genetic composition, with implications for human health and viral evolution. I propose that this field can be advanced by bringing new perspectives into virology (e.g., social evolution theory) and uniting disciplinary divides within virology (classical, host-focused, and ecoevolutionary). The development of novel high-throughput tools that meld molecular and evolutionary approaches can harness viral diversity as an experimental asset to understand complex viral social interactions. A greater knowledge of virus-virus interactions will lead to the reformulation of basic concepts of virology and advances in applied virology, with new treatments that harness interactions between viruses to fight viral and bacterial infections.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Single-cell sequencing provides clues about the host interactions of segmented filamentous bacteria (SFB)

2012 ◽  
Vol 22 (6) ◽  
pp. 1107-1119 ◽  
Author(s):  
Sünje J. Pamp ◽  
Eoghan D. Harrington ◽  
Stephen R. Quake ◽  
David A. Relman ◽  
Paul C. Blainey
Keyword(s):  
Single Cell ◽  
Filamentous Bacteria ◽  
Host Interactions ◽  
Single Cell Sequencing ◽  
Segmented Filamentous Bacteria

scholarly journals Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

2020 ◽  
Author(s):  
Kaitlyn Johnson ◽  
Grant R. Howard ◽  
Daylin Morgan ◽  
Eric A. Brenner ◽  
Andrea L. Gardner ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Single Cell ◽  
Treatment Response ◽  
Model Parameters ◽  
Data Sets ◽  
Response Dynamics ◽  
Multimodal Data ◽  
Transcriptomic Data ◽  
Treatment Regimens ◽  
New Treatment

SummaryA significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

scholarly journals Tools for Single-Cell Kinetic Analysis of Virus-Host Interactions

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0145081 ◽  
Author(s):  
Jay W. Warrick ◽  
Andrea Timm ◽  
Adam Swick ◽  
John Yin
Keyword(s):  
Kinetic Analysis ◽  
Single Cell ◽  
Cell Kinetic ◽  
Host Interactions
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