Defining signatures of arm-wise copy number change and their associated drivers in kidney cancers

Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative matrix factorisation (NMF), we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC)

Computational approaches to identify genetic interactions for cancer therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimen- tal and computational approaches undertaken both in humans and model organisms to identify these interac- tions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development

'Big data' approaches for novel anti-cancer drug discovery

Introduction: The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we review how recent advances in platform technologies and the increasing availability of biological ‘big data’ are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. We then discuss how these discoveries may be amenable to therapeutic interventions. Areas covered: We discuss the current approaches that use ‘big data’ to identify cancer drivers. These approaches include genomic sequencing, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. We review how big data is being used to assess the tractability of potential drug targets and how systems biology is being utilised to identify novel drug targets. We finish the review with an overview of available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs

A combined bioinformatics and LC-MS-based approach for the development and benchmarking of a comprehensive database of Lymnaea CNS proteins

Applications of key technologies in biomedical research, such as qRT-PCR or LC-MS-based proteomics, are generating large biological (-omics) datasets which are useful for the identification and quantification of biomarkers in any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snail Lymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of memory function and dysfunction. Here, we used a bioinformatics approach to designing and benchmarking a comprehensive central nervous system (CNS) proteomics database (LymCNS-PDB) for the identification of proteins from the CNS of Lymnaea by LC-MS-based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from a published Lymnaea transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to UniProtKB sequences for molluscan proteins, including those from Lymnaea and other molluscs. LymCNS-PDB contains 9628 identified matched proteins that were benchmarked by performing LC-MS-based proteomics analysis with proteins isolated from the Lymnaea CNS. MS/MS analysis using the LymCNS-PDB database led to the identification of 3810 proteins. Only 982 proteins were identified by using a non-specific molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis of protein interactomes involved in several CNS functions in Lymnaea, including learning and memory and age-related memory decline

Repression of transcription at DNA breaks requires cohesin throughout interphase and prevents genome instability

Cohesin subunits are frequently mutated in cancer, but how they function as tumor suppressors is unknown. Cohesin mediates sister chromatid cohesion, but this is not always perturbed in cancer cells. Here, we identify a previously unknown role for cohesin. We find that cohesin is required to repress transcription at DNA double-strand breaks (DSBs). Notably, cohesin represses transcription at DSBs throughout interphase, indicating that this is distinct from its known role in mediating DNA repair through sister chromatid cohesion. We identified a cancer-associated SA2 mutation that supports sister chromatid cohesion but is unable to repress transcription at DSBs. We further show that failure to repress transcription at DSBs leads to large-scale genome rearrangements. Cancer samples lacking SA2 display mutational patterns consistent with loss of this pathway. These findings uncover a new function for cohesin that provides insights into its frequent loss in cancer

Designing physical activity environments to accrue physical and psychological effects

Understanding how best to accrue benefits from designing physical activity and exercise programmes is needed to tackle global health problems related to physical inactivity and poor mental health. Some studies have implicated an important role for green exercise and physical activity, but there is a lack of clarity in current research. Therefore, more work is needed to understand how to design green physical activity and exercise environments that afford (invite) physical and psychological benefits to individuals. We examined whether exercising while viewing a dynamic or static image of a scene from nature would offer different affordances (invitations for behaviours to emerge), compared to the common conditions of self-selected entertainment. For this purpose, 30 participants (18 males and 12 females; age 27.5 ± 9 yrs; mass 67.6 ± 11.1 kg; stature 173.7 ± 8.2 cm) exercised in three experimental conditions in a counterbalanced design while: (i) viewing a video of a green environment, (ii) viewing a single static image of the green environment; and (iii), when using typical self-selected entertainment without viewing images of nature. A twenty-minute treadmill run was undertaken at the participants’ own self-selected speed in a laboratory while energy expenditure and psychological states (using PANAS) were assessed. Results showed no differences in energy expenditure (p > .05) or negative affect (p > .05) between conditions. However, data revealed significant differences in positive affect when participants ran with a static image and their own entertainment compared to running with a dynamic image. Results revealed how differences in affordances designed into physical activity environments can shape psychological states that emerge during exercise. Further research is needed on affordance design in physical activity and exercise by engineers, designers, planners and psychologists to explore effects of a range of simulated environments, with different target groups, such as fit and unfit individuals, elderly and children

A combined bioinformatics and LC-MS-based approach for the development and benchmarking of a comprehensive database of Lymnaea CNS proteins

Applications of key technologies in biomedical research, such as qRT-PCR or LC-MS-based proteomics, are generating large biological (-omics) datasets which are useful for the identification and quantification of biomarkers in any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snail Lymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of memory function and dysfunction. Here, we used a bioinformatics approach to designing and benchmarking a comprehensive central nervous system (CNS) proteomics database (LymCNS-PDB) for the identification of proteins from the CNS of Lymnaea by LC-MS-based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from a published Lymnaea transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to UniProtKB sequences for molluscan proteins, including those from Lymnaea and other molluscs. LymCNS-PDB contains 9628 identified matched proteins that were benchmarked by performing LC-MS-based proteomics analysis with proteins isolated from the Lymnaea CNS. MS/MS analysis using the LymCNS-PDB database led to the identification of 3810 proteins. Only 982 proteins were identified by using a non-specific molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis of protein interactomes involved in several CNS functions in Lymnaea, including learning and memory and age-related memory decline

Bioinformatics in translational drug discovery

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications

Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data).We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats

Computational analysis of patterns of DNA damage

EMBARGOED - expected end date 12.04.202