Computational methods for the analysis of non-cell-autonomous phenomena and derived gene co-expression networks

Non-cell-autonomous effects are the changes observed in one cell or cell-type as a consequence of the actions of another. The study of these phenomena is crucial to our understanding of how diverse cell-types function and co-operate together in complex tissues. The investigation of these effects has been greatly advanced by the advent of next-generation sequencing (NGS) technologies which enable the rapid sequencing of genetic information. NGS data, such as RNA-Seq, can be analysed computationally to allow comparison of cellular transcriptomes. In practice, the study of non-cellautonomous phenomena through NGS has relied upon the physical separation of cell populations in order to be sure that derived transcriptomic data is exclusively from one cell type or the other. However these methods have been shown to introduce noise as a result of stress induced by the separation process, whilst also being susceptible to bias through contamination resulting from imperfect separation of cell populations. In this thesis, a pipeline was developed to provide an in silico means of investigating these phenomena without the need for physical separation. The pipeline takes RNA-seq reads from novel mixed-species populations - in vitro cultures where each cell type is derived from a distinct species - and sorts them according to species specific origin using quality variables from multiple genome mappings as discriminators. Our method is demonstrably robust to incorrect assignment and shows high precision and recall across species of differing genetic distances, thereby providing an alternative to flawed physical separation techniques. Downstream study of such RNA-seq samples is increasingly conducted using network methodologies. Gene co-expression networks have been demonstrated as a biologically representative means for analysing NGS data. However, many existing methods for attributing the involvement of biological function to networked datasets disregard the structural information provided within them. In this thesis, I build upon an existing approach to use information theoretic entropy as a method for network-based enrichment and thereby demonstrate that the integration of network edge information can be used to more reliably infer biological pathway involvement. Our method out-performs the original whilst correcting for pathway-size bias. Lastly, the utility of the methods presented in this thesis was demonstrated through application to the study of two different phenomena: the induction of neural activity on co-cultures of neurons with astrocytes and the stimulation of microglia by LPS on co-cultures of microglia, neurons and astrocytes, by investigating cell-type specific involvement of biological pathways

Electrochromic single and two-core viologen derivatives and optical articles containing them

The present invention relates to a group of novel electrochromic materials. More specifically, it relates to electrochromic materials based on either single or two-core viologen systems and the use of these viologen systems as a variable transmittance medium for the manufacture of an optical article, such as an ophthalmic lens

Electrochromic two-core viologen derivatives and optical articles containing them

The present invention relates to a group of novel electrochromic materials. More specifically, it relates to electrochromic materials having two-core viologens and the use of these two-core viologens as a variable transmittance medium for the manufacture of an optical article, such as an ophthalmic lens

A system�based intervention to reduce Black�White disparities in the treatment of early stage lung cancer: A pragmatic trial at five cancer centers

Background: Advances in early diagnosis and curative treatment have reduced high mortality rates associated with non�small cell lung cancer. However, racial disparity in survival persists partly because Black patients receive less curative treatment than White patients. Methods: We performed a 5�year pragmatic, trial at five cancer centers using a system�based intervention. Patients diagnosed with early stage lung cancer, aged 18�85 were eligible. Intervention components included: (1) a real�time warning system derived from electronic health records, (2) race�specific feedback to clinical teams on treatment completion rates, and (3) a nurse navigator. Consented patients were compared to retrospective and concurrent controls. The primary outcome was receipt of curative treatment. Results: There were 2841 early stage lung cancer patients (16% Black) in the retrospective group and 360 (32% Black) in the intervention group. For the retrospective baseline, crude treatment rates were 78% for White patients vs 69% for Black patients (P < 0.001); difference by race was confirmed by a model adjusted for age, treatment site, cancer stage, gender, comorbid illness, and income�odds ratio (OR) 0.66 for Black patients (95% CI 0.51�0.85, P = 0.001). Within the intervention cohort, the crude rate was 96.5% for Black vs 95% for White patients (P = 0.56). Odds ratio for the adjusted analysis was 2.1 (95% CI 0.41�10.4, P = 0.39) for Black vs White patients. Between group analyses confirmed treatment parity for the intervention. Conclusion: A system�based intervention tested in five cancer centers reduced racial gaps and improved care for all

BioCPR–A Tool for Correlation Plots

A gene is a sequence of DNA bases through which genetic information is passed on to the next generation. Most genes encode for proteins that ultimately control cellular function. Understanding the interrelation between genes without the application of statistical methods can be a daunting task. Correlation analysis is a powerful approach to determine the strength of association between two variables (e.g., gene-wise expression). Moreover, it becomes essential to visualize this data to establish patterns and derive insight. The most common method for gene expression visualization is to use correlation heatmaps in which the colors of the plot represent strength of co-expression. In order to address this requirement, we developed a visualization tool called BioCPR: Biological Correlation Plots in R. This tool performs both correlation analysis and subsequent visualization in the form of an interactive heatmap, improving both usability and interpretation of the data. BioCPR is an R Shiny-based application and can be run locally in Rstudio or a web browser.</p

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species.

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species

Precambrian olistoliths masquerading as sills from Death Valley, California

Olistolith production and magmatism are processes commonly associated with extensional tectonic settings, such as rift basins. We present a cautionary exemplar from one such Precambrian basin, in which we reinterpret metabasite bodies, previously documented as sills, to be olistoliths. We nevertheless demonstrate that, on the basis of field observation alone, the previous but erroneous sill interpretation is parsimonious. Indeed, it is only by using isotopic age and compositional analysis that the true identities of these metabasite olistoliths are revealed. We present new data from metabasites and metasedimentary strata of the Kingston Peak Formation (Cryogenian) and Crystal Spring Formation (Mesoproterozoic) of Death Valley, USA. These include field observations, U?Pb apatite ages, U?Pb zircon ages (detrital and igneous) and whole-rock geochemistry. These data also provide a new maximum age for the base of the Pahrump Group and suggest that the Crystal Spring Diabase was more tholeiitic than previously thought. Similar sill/olistolith misinterpretations may have occurred elsewhere, potentially producing erroneous age and tectonic-setting interpretations of surrounding strata. This is particularly relevant in Precambrian rocks, where fossil age constraints are rare. This is illustrated herein using a potential example from the Neoproterozoic literature of the Lufilian belt, Africa. We caution others against Precambrian olistoliths masquerading as sills.publishersversionPeer reviewe

A variant of KCC2 from patients with febrile seizures impairs neuronal Cl- extrusion and dendritic spine formation

Genetic variation in SLC12A5 which encodes KCC2, the neuron‐specific cation‐chloride cotransporter that is essential for hyperpolarizing GABAergic signaling and formation of cortical dendritic spines, has not been reported in human disease. Screening of SLC12A5 revealed a co‐segregating variant (KCC2‐R952H) in an Australian family with febrile seizures. We show that KCC2‐R952H reduces neuronal Cl− extrusion and has a compromised ability to induce dendritic spines in vivo and in vitro. Biochemical analyses indicate a reduced surface expression of KCC2‐R952H which likely contributes to the functional deficits. Our data suggest that KCC2‐R952H is a bona fide susceptibility variant for febrile seizures.Peer reviewe

A variant of KCC2 from patients with febrile seizures impairs neuronal Cl- extrusion and dendritic spine formation

Genetic variation in SLC12A5 which encodes KCC2, the neuron‐specific cation‐chloride cotransporter that is essential for hyperpolarizing GABAergic signaling and formation of cortical dendritic spines, has not been reported in human disease. Screening of SLC12A5 revealed a co‐segregating variant (KCC2‐R952H) in an Australian family with febrile seizures. We show that KCC2‐R952H reduces neuronal Cl− extrusion and has a compromised ability to induce dendritic spines in vivo and in vitro. Biochemical analyses indicate a reduced surface expression of KCC2‐R952H which likely contributes to the functional deficits. Our data suggest that KCC2‐R952H is a bona fide susceptibility variant for febrile seizures.Peer reviewe

The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression

Prostate cancer is among the most common cancers in men, with a large fraction of the individual risk attributable to heritable factors. A majority of the diagnosed cases does not lead to a lethal disease, and hence biological markers that can distinguish between indolent and fatal forms of the disease are of great importance for guiding treatment decisions. Although over 300 genetic variants are known to be associated with prostate cancer risk, few have been associated with the risk of an aggressive disease. One such variant is rs77559646 located in ANO7. This variant has a dual function. It constitutes a missense mutation in the short isoform of ANO7 and a splice region mutation in full-length ANO7. In this study, we have analyzed the impact of the variant allele of rs77559646 on ANO7 mRNA splicing using a minigene splicing assay and by performing splicing analysis with the tools IRFinder (intron retention finder), rMATS (replicate multivariate analysis of transcript splicing) and LeafCutter on RNA sequencing data from prostate tissue of six rs77559646 variant allele carriers and 43 non-carriers. The results revealed a severe disruption of ANO7 mRNA splicing in rs77559646 variant allele carriers. Immunohistochemical analysis of prostate samples from patients homozygous for the rs77559646 variant allele demonstrated a loss of apically localized ANO7 protein. Our study is the first to provide a mechanistic explanation for the impact of a prostate cancer risk SNP on ANO7 protein production. Furthermore, the rs77559646 variant is the first known germline loss-of-function mutation described for ANO7. We suggest that loss of ANO7 contributes to prostate cancer progression.</p