Biology subjects

Gibbs, D.

Publications and source records attributed to Gibbs, D..

2 recordsLinked to original sources

bioRxiv2021

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

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.

plant biology↗

bioRxiv2020

A functional module states framework reveals cell states for drug and target prediction

Cells are complex systems in which many functions are performed by different genetically-defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we developed a Functional Module States framework. Using this framework, we 1) defined the drug induced transcriptional state space for breast cancer cell lines using large public gene expression datasets, and revealed that the transcriptional states are associated with drug concentration and drug targets; 2) identified potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown associated cancer dependency; and 3) used functional module states to predict transcriptional state-dependent drug sensitivity and built prediction models using the functional module states for drug response. This approach demonstrates a similar prediction performance as do approaches using high dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

systems biology↗