scholarly journals Altered collective mitochondrial dynamics in an Arabidopsis msh1 mutant compromising organelle DNA maintenance

2021 ◽  
Author(s):  
Joanna Chustecki ◽  
Ross Etherington ◽  
Daniel Gibbs ◽  
Iain Johnston
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Social Dynamics ◽  
Mitochondrial Dynamics ◽  
Organelle Dna ◽  
General Response ◽  
Dna Maintenance ◽  
Dynamic Populations ◽  
Potential Efficiency ◽  
Nuclear Features

Mitochondria form highly dynamic populations in the cells of plants (and all eukaryotes). The characteristics of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently-developed quantitative approach to reveal and analyse the physical and collective "social" dynamics of mitochondria in an Arabidopsis msh1 mutant where organelle DNA maintenance machinery is compromised. We use a newly-created line combining the msh1 mutant with mitochondrially-targeted GFP, and characterise mitochondrial dynamics with a combination of single-cell timelapse microscopy, computational tracking and network analysis. The collective physical behaviour of msh1 mitochondria is altered from wildtype in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected with greater potential efficiency for inter-organelle exchange. We find that these changes are similar to those observed in friendly, where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges.

scholarly journals NETWORK ANALYSIS AND SOCIAL DYNAMICS

2004 ◽  
Vol 35 (2-3) ◽  
pp. 173-192 ◽  
Author(s):  
DOUGLAS R. WHITE
Keyword(s):  
Network Analysis ◽  
Social Dynamics

scholarly journals Transcriptional network analysis of transcriptomic diversity in resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

2020 ◽  
Author(s):  
Kim M. Summers ◽  
Stephen J. Bush ◽  
David A. Hume
Keyword(s):  
Dendritic Cells ◽  
Network Analysis ◽  
Single Cell ◽  
Cell Types ◽  
Mononuclear Phagocyte ◽  
The Body ◽  
Mononuclear Phagocyte System ◽  
Primary Data ◽  
Analysis Tool ◽  
Rna Seq

AbstractThe mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages and dendritic cells (DC) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from NCBI-GEO >500 quality RNA-seq datasets generated from mouse MPS cells isolated from multiple tissues. The primary data were randomly down-sized to a depth of 10 million reads and requantified. The resulting dataset was clustered using the network analysis tool Graphia. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1 and cDC2, and lacking Fcgr1 (CD64), were centrally-located within the MPS cluster and no more distinct than other MPS cell types. A gene-to-gene correlation matrix identified large generic co-expression clusters associated with MPS maturation and innate immune function. Smaller co-expression gene clusters including the transcription factors that drive them showed higher expression within defined isolated cells, including macrophages and DC from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell homeostasis, a cluster of transcripts enriched in intestinal macrophages and a generic cDC cluster associated with Ccr7. However, transcripts encoding many other putative MPS subset markers including Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Retnla and H2-a/e (class II MHC) clustered idiosyncratically and were not correlated with underlying functions. The data provide no support for the concept of markers of M2 polarization or the specific adaptation of DC to present antigen to T cells. Co-expression of immediate early genes (e.g. Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (Tnf, Il1b, Ccl3/4) indicated that all tissue disaggregation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of public RNA-seq and single cell RNA-seq data from the same lung cell populations showed that the extensive heterogeneity implied by the global cluster analysis may be even greater at a single cell level with few markers strongly correlated with each other. This analysis highlights the power of large datasets to identify the diversity of MPS cellular phenotypes, and the limited predictive value of surface markers to define lineages, functions or subpopulations.

scholarly journals Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

2021 ◽  
Vol 4 ◽  
Author(s):  
Quirin Würschinger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Dynamics ◽  
Temporal Dynamics ◽  
New Words ◽  
Network Analyses ◽  
The Social ◽  
Lexical Innovation ◽  
Social Diffusion

Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.

scholarly journals Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1938 ◽  
Author(s):  
Xiucai Ye ◽  
Weihang Zhang ◽  
Yasunori Futamura ◽  
Tetsuya Sakurai
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
High Throughput Sequencing ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Cancer Subtypes ◽  
Learning Framework

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.

scholarly journals Plant Organelle DNA Maintenance

Plants ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 683 ◽  
Author(s):  
Niaz Ahmad ◽  
Brent L. Nielsen
Keyword(s):  
Copy Number ◽  
Developmental Stages ◽  
Higher Plants ◽  
Current Knowledge ◽  
Higher Plant ◽  
Organelle Dna ◽  
Number Variation ◽  
A Cell ◽  
Genome Copy Number ◽  
Dna Maintenance

Plant cells contain two double membrane bound organelles, plastids and mitochondria, that contain their own genomes. There is a very large variation in the sizes of mitochondrial genomes in higher plants, while the plastid genome remains relatively uniform across different species. One of the curious features of the organelle DNA is that it exists in a high copy number per mitochondria or chloroplast, which varies greatly in different tissues during plant development. The variations in copy number, morphology and genomic content reflect the diversity in organelle functions. The link between the metabolic needs of a cell and the capacity of mitochondria and chloroplasts to fulfill this demand is thought to act as a selective force on the number of organelles and genome copies per organelle. However, it is not yet clear how the activities of mitochondria and chloroplasts are coordinated in response to cellular and environmental cues. The relationship between genome copy number variation and the mechanism(s) by which the genomes are maintained through different developmental stages are yet to be fully understood. This Special Issue has several contributions that address current knowledge of higher plant organelle DNA. Here we briefly introduce these articles that discuss the importance of different aspects of the organelle genome in higher plants.

scholarly journals scdNet: a computational tool for single-cell differential network analysis

2018 ◽  
Vol 12 (S8) ◽  
Author(s):  
Yu-Chiao Chiu ◽  
Tzu-Hung Hsiao ◽  
Li-Ju Wang ◽  
Yidong Chen ◽  
Yu-Hsuan Joni Shao
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Computational Tool ◽  
Differential Network Analysis ◽  
Differential Network

scholarly journals ER–mitochondrial junctions can be bypassed by dominant mutations in the endosomal protein Vps13

2015 ◽  
Vol 210 (6) ◽  
pp. 883-890 ◽  
Author(s):  
Alexander B. Lang ◽  
Arun T. John Peter ◽  
Peter Walter ◽  
Benoît Kornmann
Keyword(s):  
Endoplasmic Reticulum ◽  
Mitochondrial Dynamics ◽  
Growth Conditions ◽  
Single Amino Acid ◽  
Amino Acid Substitutions ◽  
Contact Sites ◽  
Lipid Exchange ◽  
Mitochondrial Junctions ◽  
Vacuolar Protein ◽  
Dna Maintenance

The endoplasmic reticulum–mitochondria encounter structure (ERMES) complex tethers the endoplasmic reticulum and the mitochondria. It is thought to facilitate interorganelle lipid exchange and influence mitochondrial dynamics and mitochondrial DNA maintenance. Despite this important role, ERMES is not found in metazoans. Here, we identified single amino acid substitutions in Vps13 (vacuolar protein sorting 13), a large universally conserved eukaryotic protein, which suppress all measured phenotypic consequences of ERMES deficiency. Combined loss of VPS13 and ERMES is lethal, indicating that Vps13 and ERMES function in redundant pathways. Vps13 dynamically localizes to vacuole–mitochondria and to vacuole–nucleus contact sites depending on growth conditions, suggesting that ERMES function can be bypassed by the activity of other contact sites, and that contact sites establish a growth condition–regulated organelle network.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Vol 34 (17) ◽  
pp. i964-i971 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Cell Network
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