Has gene duplication impacted the evolution of Eutherian longevity?

One of the greatest unresolved questions in aging biology is determining the genetic basis of interspecies longevity variation. Gene duplication is often the key to understanding the origin and evolution of important Eutherian phenotypes. We systematically identified longevity‐associated genes in model organisms that duplicated throughout Eutherian evolution. Longevity‐associated gene families have a marginally significantly higher rate of duplication compared to non‐longevity‐associated gene families. Anti‐longevity‐associated gene families have significantly increased rate of duplication compared to pro‐longevity gene families and are enriched in neurodegenerative disease categories. Conversely, duplicated pro‐longevity‐associated gene families are enriched in cell cycle genes. There is a cluster of longevity‐associated gene families that expanded solely in long‐lived species that is significantly enriched in pathways relating to 3‐UTR‐mediated translational regulation, metabolism of proteins and gene expression, pathways that have the potential to affect longevity. The identification of a gene cluster that duplicated solely in long‐lived species involved in such fundamental processes provides a promising avenue for further exploration of Eutherian longevity evolution

UN gender network workshop III: report

Report of the UN Gender Network's Third Workshop at Durham Universit

United Nations gender network: second workshop report

Report of the Second UN Gender Network Workshop

Increased genome sampling reveals a dynamic relationship between gene duplicability and the structure of the primate protein-protein interaction network. Mol Biol Evol

Abstract Although gene duplications occur at a higher rate, only a small fraction of these are retained. The position of a gene's encoded product in the protein-protein interaction network has recently emerged as a determining factor of gene duplicability. However, the direction of the relationship between network centrality and duplicability is not universal: In Escherichia coli, yeast, fly, and worm, duplicated genes more often act at the periphery of the network, whereas in humans, such genes tend to occupy the most central positions. Herein, we have inferred duplication events that took place in the different branches of the primate phylogeny. In agreement with previous observations, we found that duplications generally affected the most central network genes, which is presumably the process that has most influenced the trend in humans. However, the opposite trend-that is, duplication being more common in genes whose encoded products are peripheral in the network-is observed for three recent branches, including, quite counterintuitively, the external branch leading to humans. This indicates a shift in the relationship between centrality and duplicability during primate evolution. Furthermore, we found that genes encoding interacting proteins exhibit phylogenetic tree topologies that are more similar than expected for random pairs and that genes duplicated in a given branch of the phylogeny tend to interact with those that duplicated in the same lineage. These results indicate that duplication of a gene increases the likelihood of duplication of its interacting partners. Our observations indicate that the structure of the primate protein-protein interaction network affects gene duplicability in previously unrecognized ways

A scan for genes associated with cancer mortality and longevity in pedigree dog breeds.

Selective breeding of the domestic dog (Canis lupus familiaris) rigidly retains desirable features, and could inadvertently fix disease-causing variants within a breed. We combine phenotypic data from > 72,000 dogs with a large genotypic dataset to search for genes associated with cancer mortality and longevity in pedigree dog breeds. We validated previous findings that breeds with higher average body weight have higher cancer mortality rates and lower life expectancy. We identified a significant positive correlation between life span and cancer mortality residuals corrected for body weight, implying that long-lived breeds die more frequently from cancer compared to short-lived breeds. We replicated a number of known genetic associations with body weight (IGF1, GHR, CD36, SMAD2 and IGF2BP2). Subsequently, we identified five genetic variants in known cancer-related genes (located within SIPA1, ADCY7 and ARNT2) that could be associated with cancer mortality residuals corrected for confounding factors. One putative genetic variant was marginally significantly associated with longevity residuals that had been corrected for the effects of body weight; this genetic variant is located within PRDX1, a peroxiredoxin that belongs to an emerging class of pro-longevity associated genes. This research should be considered as an exploratory analysis to uncover associations between genes and longevity/cancer mortality

Ageing transcriptome meta-analysis reveals similarities between key mammalian tissues

By combining transcriptomic data with other data sources, inferences can be made about functional changes during ageing. Thus, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, identifying a transcriptomic signature of ageing across species and tissues. Analyses on subsets of these datasets produced transcriptomic signatures of ageing for brain, heart and muscle. We then applied enrichment analysis and machine learning to functionally describe these signatures, revealing overexpression of immune and stress response genes and underexpression of metabolic and developmental genes. Further analyses revealed little overlap between genes differentially expressed with age in different tissues, despite ageing differentially expressed genes typically being widely expressed across tissues. Additionally we show that the ageing gene expression signatures (particularly the overexpressed signatures) of the whole meta-analysis, brain and muscle tend to include genes that are central in protein-protein interaction networks. We also show that genes underexpressed with age in the brain are highly central in a co-expression network, suggesting that underexpression of these genes may have broad phenotypic consequences. In sum, we show numerous functional similarities between the ageing transcriptomes of these important tissues, along with unique network properties of genes differentially expressed with age in both a protein-protein interaction and co-expression networks

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization

A new approach for interpreting Random Forest models and its application to the biology of ageing

This work uses the Random Forest (RF) classification algorithm to predict if a gene is over-expressed, under-expressed or has no change in expression with age in the brain. RFs have high predictive power, and RF models can be interpreted using a feature (variable) importance measure. However, current feature importance measures evaluate a feature as a whole (all feature values). We show that, for a popular type of biological data (Gene Ontology-based), usually only one value of a feature is particularly important for classification and the interpretation of the RF model. Hence, we propose a new algorithm for identifying the most important and most informative feature values in an RF model