With advances in molecular technology, the study of human ageing has turned to DNA for answers as to how humans age. Due to the size of the human genome and the longevity of humans, organisms with smaller genomes and shorter lifespans have frequently been the center of research studies in ageing. Studies of Drosophila melanogaster, Caenorhabditis elegans, yeast, and mice have uncovered specific genes that up and down regulate with age and stress. Research has yet to produce, however, results from an organism known for its longevity. Amblyomma americanum is an excellent candidate for this, as it can survive for years unfed. Two groups of 75 unfed adult A. americanum were monitored in a control environment of 85% relative humidity and an experimental environment designed to induce physiological stress at 75% relative humidity. Five ticks were tested for transcript abundance of five candidate ageing genes initially and at the 25th, 75th, and 95th percent mortality. These tests provided evidence that Amblyomma americanum undergoes changes in gene expression with age on a genetic level.

With advances in molecular technology, the study of human ageing has turned to DNA for answers as to how humans age. Due to the size of the human genome and the longevity of humans, organisms with smaller genomes and shorter lifespans have frequently been the center of research studies in ageing. Studies of Drosophila melanogaster, Caenorhabditis elegans, yeast, and mice have uncovered specific genes that up and down regulate with age and stress. Research has yet to produce, however, results from an organism known for its longevity. Amblyomma americanum is an excellent candidate for this, as it can survive for years unfed. Two groups of 75 unfed adult A. americanum were monitored in a control environment of 85% relative humidity and an experimental environment designed to induce physiological stress at 75% relative humidity. Five ticks were tested for transcript abundance of five candidate ageing genes initially and at the 25th, 75th, and 95th percent mortality. These tests provided evidence that Amblyomma americanum undergoes changes in gene expression with age on a genetic level.

The human brain’s transcriptome undergoes substantial changes over the lifespan and shows characteristic patterns that reflect anatomical regions and cellular compositions. In this thesis, I applied combinations of network algorithms and tools from computational biology to analyse transcriptional networks and their key regulators in the human brain across space (brain regions) and time (the human life course). First, I identified an age-dependent transcriptional network enriched for microglial markers. The microglia network recapitulated haematopoietic master regulators that are crucial for early microglia development using data from the ageing human brain only. In the second project, I demonstrated that gene clusters linked to neurogenesis during fetal life show moderate to strong preservation in the human adult brain. In addition to temporal development, I analysed transcriptional network dynamics across the spatial axis and detected a network of ion channel/transporter genes that express their longest 3’ untranslated regions (3’UTRs) exclusively in the brain. Enrichment for predicted miRNA response elements that are often shared among the ion channel/transporter genes, along with increased co-expression of this gene set, indicated that the extended 3’UTRs could serve as a hub for an endogenous competitive RNA network. I extended the analysis to differentiate between brain regions and additional regulatory RNA elements, namely long noncoding (lnc) RNAs. I found that genes in the hypothalamus express a region-specific network to which are also associated co-expressed lncRNAs. Finally, I added global metrics to the analysis of local networks. The dynamics of global network metrics indicated strong coordination of expression across the lifespan compared to similar variance within age groups. This work shows that the transcriptome of human post-mortem brains at least partially preserves the network structure of cell types and functionally related genes, and how it may be dissected using suitable combinations of bioinformatic algorithms.