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

A direct communication proposal to test the Zoo Hypothesis

Whether we are alone in the universe is one of the greatest mysteries facing humankind. Given the >100 billion stars in our galaxy, many have argued that it is statistically unlikely that life, including intelligent life, has not emerged anywhere else. The lack of any sign of extraterrestrial intelligence, even though on a cosmic timescale extraterrestrial civilizations would have enough time to cross the galaxy, is known as Fermi's Paradox. One possible explanation for Fermi's Paradox is the Zoo Hypothesis which states that one or more extraterrestrial civilizations know of our existence and can reach us, but have chosen not to disturb us or even make their existence known to us. I propose here a proactive test of the Zoo Hypothesis. Specifically, I propose to send a message using television and radio channels to any extraterrestrial civilization(s) that might be listening and inviting them to respond. Even though I accept this is unlikely to be successful in the sense of resulting in a response from extraterrestrial intelligences, the possibility that extraterrestrial civilizations are monitoring us cannot be dismissed and my proposal is consistent with current scientific knowledge. Besides, issuing an invitation is technically feasible, cheap and safe, and few would deny the profound importance of establishing contact with one or more extraterrestrial intelligences. A website has been set up (http://active-seti.info) to encourage discussion of this proposal and for drafting the invitation message.Comment: 16 page

Using deep learning to associate human genes with age-related diseases

Motivation: One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pre-compiled list of human genes associated with age-related diseases and apply a novel Deep Neural Network (DNN) method to find associations between gene descriptors (e.g. Gene Ontology terms, protein–protein interaction data and biological pathway information) and age-related diseases. Results: The novelty of our new DNN method is its modular architecture, which has the capability of combining several sources of biological data to predict which ageing-related diseases a gene is associated with (if any). Our DNN method achieves better predictive performance than standard DNN approaches, a Gradient Boosted Tree classifier (a strong baseline method) and a Logistic Regression classifier. Given the DNN model produced by our method, we use two approaches to identify human genes that are not known to be associated with age-related diseases according to our dataset. First, we investigate genes that are close to other disease-associated genes in a complex multi-dimensional feature space learned by the DNN algorithm. Second, using the class label probabilities output by our DNN approach, we identify genes with a high probability of being associated with age-related diseases according to the model. We provide evidence of these putative associations retrieved from the DNN model with literature support. The source code and datasets can be found at: https://github.com/fabiofabris/Bioinfo2019

Interpretable Ensembles of Classifiers for Uncertain Data with Bioinformatics Applications

Data uncertainty remains a challenging issue in many applications, but few classification algorithms can effectively cope with it. An ensemble approach for uncertain categorical features has recently been proposed, achieving promising results. It consists in biasing the sampling of features for each model in an ensemble so that less uncertain features are more likely to be sampled. Here we extend this idea of biased sampling and propose two new approaches: one for selecting training instances for each model in an ensemble and another for sampling features to be considered when splitting a node in a Random Forest training. We applied these approaches to classify ageing-related genes and predict drugs' side effects based on uncertain features representing protein-protein and protein-chemical interactions. We show that ensembles based on our proposed approaches achieve better predictive performance. In particular, our proposed approaches improved the performance of a Random Forest based on the most sophisticated approach for handling uncertain data in ensembles of this kind. Furthermore, we propose two new approaches for interpreting an ensemble of Naive Bayes classifiers and analyse their results on our datasets of ageing-related genes and drug's side effects

Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy

To date, with well over 100 different types of RNA modifications associated with various molecular functions identified on diverse types of RNA molecules, the epitranscriptome has emerged to be an important layer for gene expression regulation. It is of crucial importance and increasing interest to understand how the epitranscriptome is regulated to facilitate different biological functions from a global perspective, which may be carried forward by finding biologically meaningful epitranscriptome modules that respond to upstream epitranscriptome regulators and lead to downstream biological functions; however, due to the intrinsic properties of RNA molecules, RNA modifications, and relevant sequencing technique, the epitranscriptome profiled from high-throughput sequencing approaches often suffers from various artifacts, jeopardizing the effectiveness of epitranscriptome modules identification when using conventional approaches. To solve this problem, we developed a convenient measurement weighting strategy, which can largely tolerate the artifacts of high-throughput sequencing data. We demonstrated on real data that the proposed measurement weighting strategy indeed brings improved performance in epitranscriptome module discovery in terms of both module accuracy and biological significance. Although the new approach is integrated with Euclidean distance measurement in a hierarchical clustering scenario, it has great potential to be extended to other distance measurements and algorithms as well for addressing various tasks in epitranscriptome analysis. Additionally, we show for the first time with rigorous statistical analysis that the epitranscriptome modules are biologically meaningful with different GO functions enriched, which established the functional basis of epitranscriptome modules, fulfilled a key prerequisite for functional characterization, and deciphered the epitranscriptome and its regulation

miRNA-31 Improves Cognition and Abolishes Amyloid-beta Pathology by Targeting APP and BACE1 in an Animal Model of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia worldwide, characterized by progressive memory impairment, behavioral changes, and, ultimately, loss of consciousness and death. Recently, microRNA (miRNA) dysfunction has been associated with increased production and impaired clearance of amyloid-β (Aβ) peptides, whose accumulation is one of the most well-known pathophysiological markers of this disease. In this study, we identified several miRNAs capable of targeting key proteins of the amyloidogenic pathway. The expression of one of these miRNAs, miR-31, previously found to be decreased in AD patients, was able to simultaneously reduce the levels of APP and Bace1 mRNA in the hippocampus of 17-month-old AD triple-transgenic (3xTg-AD) female mice, leading to a significant improvement of memory deficits and a reduction in anxiety and cognitive inflexibility. In addition, lentiviral-mediated miR-31 expression significantly ameliorated AD neuropathology in this model, drastically reducing Aβ deposition in both the hippocampus and subiculum. Furthermore, the increase of miR-31 levels was enough to reduce the accumulation of glutamate vesicles in the hippocampus to levels found in non-transgenic age-matched animals. Overall, our results suggest that miR-31-mediated modulation of APP and BACE1 can become a therapeutic option in the treatment of AD.This work was financed by the European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under the project CENTRO-01-0145-FEDER-000008: BrainHealth 2020 and through the COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation and Portuguese national funds via FCT - Fundação para a Ciência e a Tecnologia, under the project POCI- 01-0145-FEDER-007440 (reference UID/NEU/04539/2013). This work was also supported by the FCT Investigator Programme (IF/ 00694/2013 to J.P.), the Marie Curie Carrier Integration Grant (PCIG13-GA-2013-618525 to J.P.), HEALTHYAGING 2020 (CENTRO-01-0145-FEDER-000012 to A.T.B.-V.), and Bial Foundation Grant 264/16. A.T.B.-V., J.G., and A.L.C. are recipients of fellowships from the FCT (Grants PTDC/BIM-MEC/0651/2012, SFRH/ BPD/120611/2016, and SFRH/BPD/108312/2015)

Els artistes i la guerra

Abstract not availabl