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Urban, D. L.

Publications and source records attributed to Urban, D. L..

2 recordsLinked to original sources

A stagewise response to mitochondrial dysfunction in mitochondrial DNA maintenance disorders

Mitochondrial DNA deletions clonally expand in skeletal muscle of patients with mtDNA maintenance disorders, impairing mitochondrial oxidative phosphorylation dysfunction. Previously we have shown that these mtDNA deletions originally arise and accumulate in the perinuclear mitochondria causing localised mitochondrial dysfunction before spreading through the muscle fibre. We believe that mito-nuclear signalling is a key contributor in this process. To further understand the role of mito-nuclear signalling, we use imaging mass cytometry to characterise the levels of mitochondrial respiratory complexes I-IV and ATP synthase alongside a mitochondrial mass marker, in a cohort of patients with mtDNA maintenance dosirders. We then expanded this panel to include protein markers of key signalling pathways to investigate the cellular response in fibres with different combinations of oxidative phosphorylation dysfunction and in ragged red fibres. We find CI and CIV deficiency to be most common, with a smaller proportion of cells that are also CIII and/or CV deficient. Interestingly, we also note that in cells deficient for one or more complexes, any complexes which are not deficient are commonly upregulated beyond the increase of mitochondrial mass typically observed in ragged red fibres. We further find that oxidative phosphorylation deficient fibres exhibit an increase in abundance of proteins involved in the proteostasis e.g. HSP60 and LONP1, and mitochondrial protein synthesis e.g. PHB1. Our analysis suggests that the cellular response to mitochondrial dysfunction changes depending on the combination of deficient oxidative phosphorylation complexes in each cell.

cell biology↗

Using genotype-environment associations to identify multilocus local adaptation

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyze many loci simultaneously, may be better suited to these data since they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods, and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations showed a superior combination of low false positive and high true positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes, and levels of population structure tested. The value of combining detections from different methods was variable, and depended on study goals and knowledge of the drivers of selection. Reanalysis of genomic data from gray wolves highlighted the unique, covarying sets of adaptive loci that could be identified using redundancy analysis, a constrained ordination. Although additional testing is needed, this study indicates that constrained ordinations are an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

genomics↗