Cell lineage-specific mitochondrial resilience during mammalian organogenesis

Mitochondrial activity differs markedly between organs, but it is not known how and when this arises. Here we show that cell lineage-specific expression profiles involving essential mitochondrial genes emerge at an early stage in mouse development, including tissue-specific isoforms present before organ formation. However, the nuclear transcriptional signatures were not independent of organelle function. Genetically disrupting intra-mitochondrial protein synthesis with two different mtDNA mutations induced cell lineage-specific compensatory responses, including molecular pathways not previously implicated in organellar maintenance. We saw downregulation of genes whose expression is known to exacerbate the effects of exogenous mitochondrial toxins, indicating a transcriptional adaptation to mitochondrial dysfunction during embryonic development. The compensatory pathways were both tissue and mutation specific and under the control of transcription factors which promote organelle resilience. These are likely to contribute to the tissue specificity which characterizes human mitochondrial diseases and are potential targets for organ-directed treatments

Confidence in protein interaction networks

Protein interaction networks are a commonly used tool in bioinformatics, e.g. for the purposes of gene function prediction or drug target identification. They are built from often heterogeneous and error-prone protein-protein interaction data. In this thesis we study the effects of data uncertainty on the structure of protein interaction networks and on downstream network analysis. Some databases provide confidence scores for protein-protein interactions, and networks are built from the data after a minimum score cut-off, or threshold, is applied. We study the effects of threshold choice on network structure. We argue that robust, biologically-relevant network analysis results should be replicated across networks obtained at different thresholds, and develop a methodology for quantifying this robustness in the context of node metrics. Our results indicate that the same node metrics are robust across a range of protein interaction networks, but are not necessarily robust in synthetic networks. We further investigate uncertain networks as a possible approach to incorporating confidence scores explicitly into network analysis. Uncertain networks are a way of conceptualising the difference between the "true" network of biologically-relevant protein-protein interactions and the observed scored data. We show that any inference on the structure of the "true" network is strongly influenced by assumptions made about the dependence - or lack thereof - between edges in the scored network. Finally, we focus on networks constructed from gene co-expression data. Gene co-expression can be measured in a number of different ways. Moreover, when networks are constructed, different thresholds can be applied to the co-expression values. It is not always clear which network construction method should be preferred. We develop a software package, COGENT, designed to aid network construction choice without the need for external validation data.</p

COGENT: evaluating the consistency of gene co-expression networks

Gene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data

Robust gene coexpression networks using signed distance correlation

Motivation Even within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information. Results We introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation and mutual information. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson correlation or mutual information. Supplementary information Supplementary Information and code are available at Bioinformatics and https://github.com/javier-pardodiaz/sdcorGCN online